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-rw-r--r--dev-python/pyds9/Manifest3
-rw-r--r--dev-python/pyds9/metadata.xml10
-rw-r--r--dev-python/pyds9/pyds9-1.0-syslibs.patch74
-rw-r--r--dev-python/pyds9/pyds9-1.0.ebuild18
-rw-r--r--dev-python/pymvpa/metadata.xml15
-rw-r--r--dev-python/pymvpa/pymvpa-0.4.4.ebuild37
-rw-r--r--dev-python/pysao/Manifest1
-rw-r--r--dev-python/pysao/pysao-9999.ebuild21
-rw-r--r--docs/proj/#linalg.xml#551
-rw-r--r--docs/proj/linalg.xml550
-rw-r--r--media-libs/avbin/Manifest6
-rw-r--r--media-libs/avbin/avbin-7.ebuild58
-rw-r--r--media-libs/avbin/files/avbin-7-ffmpeg05.patch66
-rw-r--r--media-libs/avbin/files/avbin-7-sample24.patch15
-rw-r--r--media-libs/avbin/files/avbin-7-swscale.patch45
-rw-r--r--media-libs/avbin/metadata.xml9
-rw-r--r--sci-libs/levmar/Manifest2
-rw-r--r--sci-libs/levmar/levmar-2.5.ebuild36
-rw-r--r--sci-libs/levmar/levmar-2.5/Axb.c74
-rw-r--r--sci-libs/levmar/levmar-2.5/Axb.obin0 -> 34152 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/Axb_core.c1144
-rw-r--r--sci-libs/levmar/levmar-2.5/CMakeLists.txt54
-rw-r--r--sci-libs/levmar/levmar-2.5/LICENSE340
-rw-r--r--sci-libs/levmar/levmar-2.5/Makefile47
-rw-r--r--sci-libs/levmar/levmar-2.5/Makefile.icc62
-rw-r--r--sci-libs/levmar/levmar-2.5/Makefile.so49
-rw-r--r--sci-libs/levmar/levmar-2.5/Makefile.vc59
-rw-r--r--sci-libs/levmar/levmar-2.5/README.txt74
-rw-r--r--sci-libs/levmar/levmar-2.5/compiler.h45
-rw-r--r--sci-libs/levmar/levmar-2.5/expfit.c122
-rw-r--r--sci-libs/levmar/levmar-2.5/levmar.h370
-rw-r--r--sci-libs/levmar/levmar-2.5/levmar.vcproj200
-rw-r--r--sci-libs/levmar/levmar-2.5/liblevmar.abin0 -> 183478 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/lm.c83
l---------sci-libs/levmar/levmar-2.5/lm.h1
-rw-r--r--sci-libs/levmar/levmar-2.5/lm.obin0 -> 25104 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/lm_core.c843
-rw-r--r--sci-libs/levmar/levmar-2.5/lmbc.c85
-rw-r--r--sci-libs/levmar/levmar-2.5/lmbc.obin0 -> 33480 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/lmbc_core.c948
-rw-r--r--sci-libs/levmar/levmar-2.5/lmblec.c87
-rw-r--r--sci-libs/levmar/levmar-2.5/lmblec.obin0 -> 14504 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/lmblec_core.c413
-rw-r--r--sci-libs/levmar/levmar-2.5/lmbleic.c89
-rw-r--r--sci-libs/levmar/levmar-2.5/lmbleic.obin0 -> 18376 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/lmbleic_core.c506
-rw-r--r--sci-libs/levmar/levmar-2.5/lmdemo.c1230
-rw-r--r--sci-libs/levmar/levmar-2.5/lmdemo.obin0 -> 37536 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/lmdemo.vcproj194
-rw-r--r--sci-libs/levmar/levmar-2.5/lmlec.c80
-rw-r--r--sci-libs/levmar/levmar-2.5/lmlec.obin0 -> 26752 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/lmlec_core.c657
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/CMakeLists.txt58
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/Makefile30
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/Makefile.w3226
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/README.txt35
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/bt3.m11
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/expfit.m8
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/hs01.m6
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/jacbt3.m13
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/jacexpfit.m7
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/jachs01.m5
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/jacmeyer.m10
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/jacmodhs52.m7
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/jacmodhs76.m7
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/jacosborne.m11
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/levmar.c677
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/levmar.m84
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/lmdemo.m140
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/meyer.m9
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/modhs52.m7
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/modhs76.m7
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/mods235.m5
-rw-r--r--sci-libs/levmar/levmar-2.5/matlab/osborne.m7
-rw-r--r--sci-libs/levmar/levmar-2.5/misc.c70
-rw-r--r--sci-libs/levmar/levmar-2.5/misc.h106
-rw-r--r--sci-libs/levmar/levmar-2.5/misc.obin0 -> 29280 bytes
-rw-r--r--sci-libs/levmar/levmar-2.5/misc_core.c813
-rw-r--r--sci-libs/lpsolve/Manifest2
-rw-r--r--sci-libs/lpsolve/lpsolve-5.5.0.15.ebuild53
-rw-r--r--sci-libs/pagmo/Manifest1
-rw-r--r--sci-libs/pagmo/pagmo-9999.ebuild35
-rw-r--r--sci-libs/shogun/Manifest4
-rw-r--r--sci-libs/shogun/files/shogun-0.9.3-lapack.patch18
-rw-r--r--sci-libs/shogun/metadata.xml22
-rw-r--r--sci-libs/shogun/shogun-0.9.3.ebuild64
m---------sci-physics/fedora0
-rw-r--r--sci-physics/healpix/Manifest3
-rw-r--r--sci-physics/healpix/healpix-2.15a.ebuild33
-rw-r--r--sci-physics/healpix/metadata.xml11
-rw-r--r--sci-physics/root-9999.ebuild18
91 files changed, 11796 insertions, 0 deletions
diff --git a/dev-python/pyds9/Manifest b/dev-python/pyds9/Manifest
new file mode 100644
index 000000000..f252ebe7c
--- /dev/null
+++ b/dev-python/pyds9/Manifest
@@ -0,0 +1,3 @@
+DIST pyds9-1.0.tar.gz 869723 RMD160 da335e04264c97b46853a0849e9717f773e01046 SHA1 3debdeba0c54a8129a2a277be98d44969a4c9db6 SHA256 4184380aa383a6cbf2d5227ea83dcc0c3dc9c6d0952b81d5c416d782ffff572f
+EBUILD pyds9-1.0.ebuild 422 RMD160 d494b0419edb9dfdc398556a7ae4f33afe4a28ce SHA1 2de3873833ddd8bdfbc34b72f5eaecdc7a0fee0f SHA256 bad88d838fc35472400c4068b633d311b55bd7665f68bf36e685a9e917570206
+MISC metadata.xml 374 RMD160 751b95b8cb2d0fa8194e7388b51f5785b4f16d0f SHA1 32790eaab968b07b66c22157f42138b1f56911e6 SHA256 5fe68de0cf26109619592c99a227ee9f4b54f2a18e4c9f95206a63c8275c1b60
diff --git a/dev-python/pyds9/metadata.xml b/dev-python/pyds9/metadata.xml
new file mode 100644
index 000000000..03c443f53
--- /dev/null
+++ b/dev-python/pyds9/metadata.xml
@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+<herd>sci</herd>
+<longdescription lang="en">
+ pywcs is a set of routines for handling the FITS World Coordinate
+ System (WCS) standard. It is a thin wrapper around the high- and
+ mid-level interfaces for wcslib.
+</longdescription>
+</pkgmetadata>
diff --git a/dev-python/pyds9/pyds9-1.0-syslibs.patch b/dev-python/pyds9/pyds9-1.0-syslibs.patch
new file mode 100644
index 000000000..b4e7e5b75
--- /dev/null
+++ b/dev-python/pyds9/pyds9-1.0-syslibs.patch
@@ -0,0 +1,74 @@
+--- setup.py.orig 2010-07-20 17:30:06.000000000 +0100
++++ setup.py 2010-07-20 17:43:40.000000000 +0100
+@@ -1,60 +1,5 @@
+ #!/usr/bin/env python
+ from distutils.core import setup
+-from distutils.command import build_py, install_data, clean
+-from os import system, path
+-import os
+-import struct
+-
+-# which shared library?
+-ulist=os.uname()
+-if ulist[0] == 'Darwin':
+- xpalib = 'libxpa.dylib'
+-else:
+- xpalib = 'libxpa.so'
+-
+-# make command for xpa
+-xpadir='xpa-2.1.10'
+-def make(which):
+- curdir=os.getcwd()
+- srcDir=os.path.join(os.path.dirname(os.path.abspath(__file__)),xpadir)
+- os.chdir(srcDir)
+- if which == 'all':
+- os.system('echo "building XPA shared library ..."')
+- cflags=''
+- if not 'CFLAGS' in os.environ and struct.calcsize("P") == 4:
+- if ulist[0] == 'Darwin' or ulist[4] == 'x86_64':
+- os.system('echo "adding -m32 to compiler flags ..."')
+- cflags=' CFLAGS="-m32"'
+- os.system('./configure --enable-shared --without-tcl'+cflags)
+- os.system('make clean; make; rm -f *.o')
+- elif which == 'clean':
+- os.system('echo "cleaning XPA ..."')
+- os.system('make clean')
+- os.chdir(curdir)
+-
+-# rework build_py to make the xpa shared library as well
+-class my_build_py(build_py.build_py):
+- def run(self):
+- make('all')
+- build_py.build_py.run(self)
+-
+-# thanks to setup.py in ctypes
+-class my_install_data(install_data.install_data):
+- """A custom install_data command, which will install it's files
+- into the standard directories (normally lib/site-packages).
+- """
+- def finalize_options(self):
+- if self.install_dir is None:
+- installobj = self.distribution.get_command_obj('install')
+- self.install_dir = installobj.install_lib
+- print 'Installing data files to %s' % self.install_dir
+- install_data.install_data.finalize_options(self)
+-
+-# clean up xpa as well
+-class my_clean(clean.clean):
+- def run(self):
+- make('clean')
+- clean.clean.run(self)
+
+ # setup command
+ setup(name='pyds9',
+@@ -63,9 +8,5 @@
+ author='Bill Joye and Eric Mandel',
+ author_email='saord@cfa.harvard.edu',
+ url='http://hea-www.harvard.edu/saord/ds9/',
+- py_modules=['ds9', 'xpa'],
+- data_files=[('', [xpadir+'/'+xpalib, xpadir+'/xpans'])],
+- cmdclass = {'build_py': my_build_py, \
+- 'install_data': my_install_data, \
+- 'clean': my_clean },
++ py_modules=['ds9', 'xpa']
+ )
diff --git a/dev-python/pyds9/pyds9-1.0.ebuild b/dev-python/pyds9/pyds9-1.0.ebuild
new file mode 100644
index 000000000..f41f6be4c
--- /dev/null
+++ b/dev-python/pyds9/pyds9-1.0.ebuild
@@ -0,0 +1,18 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+EAPI=3
+inherit distutils
+
+DESCRIPTION="Python wrapper around SAOImage DS9"
+HOMEPAGE="http://hea-www.harvard.edu/saord/ds9/pyds9/"
+SRC_URI="http://hea-www.harvard.edu/saord/download/ds9/python/${P}.tar.gz"
+
+LICENSE=""
+SLOT="0"
+KEYWORDS="~amd64 ~x86"
+IUSE=""
+
+RDEPEND="x11-libs/xpa"
+DEPEND="${RDEPEND}"
diff --git a/dev-python/pymvpa/metadata.xml b/dev-python/pymvpa/metadata.xml
new file mode 100644
index 000000000..6b37a236c
--- /dev/null
+++ b/dev-python/pymvpa/metadata.xml
@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+<herd>sci-mathematics</herd>
+<herd>python</herd>
+<longdescription lang="en">
+ Python module to ease pattern classification analyses of large
+ datasets. It provides high-level abstraction of typical processing
+ steps (e.g. data preparation, classification, feature selection,
+ generalization testing), a number of implementations of some popular
+ algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic
+ Regression), and bindings to external machine learning libraries (libsvm,
+ shogun).
+</longdescription>
+</pkgmetadata>
diff --git a/dev-python/pymvpa/pymvpa-0.4.4.ebuild b/dev-python/pymvpa/pymvpa-0.4.4.ebuild
new file mode 100644
index 000000000..06c6e27a7
--- /dev/null
+++ b/dev-python/pymvpa/pymvpa-0.4.4.ebuild
@@ -0,0 +1,37 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: /var/cvsroot/gentoo-x86/dev-python/pywavelets/pywavelets-0.2.0.ebuild,v 1.1 2010/04/22 20:14:30 bicatali Exp $
+
+EAPI=2
+SUPPORT_PYTHON_ABIS="1"
+inherit distutils
+
+DESCRIPTION="Multivariate pattern analysis with Python"
+HOMEPAGE="http://www.pymvpa.org/"
+SRC_URI=""
+LICENSE=""
+SLOT="0"
+KEYWORDS="~amd64 ~x86"
+IUSE="doc examples minimal test"
+
+DEPEND="dev-lang/swig
+ dev-python/lxml
+ dev-python/numpy
+ sys-apps/help2man
+ doc? ( dev-python/sphinx media-gfx/graphviz )
+ test? ( dev-python/nose )"
+
+RDEPEND="dev-python/numpy
+ !minimal? (
+ dev-python/hcluster
+ dev-python/ipython
+ dev-python/matplotlib
+ dev-python/pynifti
+ dev-python/rpy
+ sci-libs/afni
+ sci-libs/fsl
+ sci-libs/libsvm
+ sci-libs/scipy
+ sci-libs/shogun[python] )
+
+RESTRICT_PYTHON_ABIS="3.*"
diff --git a/dev-python/pysao/Manifest b/dev-python/pysao/Manifest
new file mode 100644
index 000000000..7ea2e6bb1
--- /dev/null
+++ b/dev-python/pysao/Manifest
@@ -0,0 +1 @@
+EBUILD pysao-9999.ebuild 449 RMD160 a4edc0920406641d736fec434ea2d10d6f9827fe SHA1 7570ae7adf0b17a8a9d147e3bd9259ff28e2b8b3 SHA256 ec1ab487379140c32b8f3ada2bf79a067ed6a98dfca1b15b954599bb81690423
diff --git a/dev-python/pysao/pysao-9999.ebuild b/dev-python/pysao/pysao-9999.ebuild
new file mode 100644
index 000000000..a63e8c03b
--- /dev/null
+++ b/dev-python/pysao/pysao-9999.ebuild
@@ -0,0 +1,21 @@
+# Copyright 1999-2009 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+inherit subversion distutils
+
+DESCRIPTION="Python wrapper around some SAO tools"
+HOMEPAGE="http://code.google.com/p/python-sao/"
+ESVN_REPO_URI="http://python-sao.googlecode.com/svn/trunk"
+
+LICENSE="MIT"
+SLOT="0"
+KEYWORDS="~amd64 ~x86"
+IUSE=""
+
+RDEPEND="x11-libs/xpa"
+DEPEND="${RDEPEND}"
+
+src_unpack() {
+ subversion_src_unpack
+}
diff --git a/docs/proj/#linalg.xml# b/docs/proj/#linalg.xml#
new file mode 100644
index 000000000..3105039cc
--- /dev/null
+++ b/docs/proj/#linalg.xml#
@@ -0,0 +1,551 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE guide SYSTEM "/dtd/guide.dtd">
+<!-- $Header: $ -->
+
+<guide link="/proj/en/science/linalg.xml">
+<title>Linear Algebra on Gentoo</title>
+
+<author title="Author">
+ <mail link="bicatali@gentoo.org">Sébastien Fabbro</mail>
+</author>
+
+<abstract>
+ This guide explains the use of linear algebra libraries and focus on
+ how to use the different implementations of BLAS and LAPACK available on Gentoo.
+</abstract>
+
+<!-- The content of this document is licensed under the CC-BY-SA license -->
+<!-- See http://creativecommons.org/licenses/by-sa/2.5 -->
+<license/>
+
+<version>1.0</version>
+<date>2010-12-22</date>
+
+<chapter>
+<title>Introduction</title>
+<section>
+<body>
+
+<p>
+ There are <uri link="http://en.wikipedia.org/wiki/List_of_numerical_libraries">many</uri>
+ performant numerical libraries available.
+ The Basic Linear Algebra Subprograms (BLAS) and the Linear Algebra PACKage (LAPACK)
+ are well designed linear algebra libraries developed by the
+ High Performance Computing (HPC) community. BLAS is an API of dense
+ matrix and vectors products, while LAPACK provides routines for
+ solving systems of linear equations. Both are widely used in
+ many scientific applications and it is, therefore, important to
+ have efficient implementations available.
+</p>
+
+<p>
+ BLAS and LAPACK were originally written in FORTRAN 77. Since then, a
+ number of additional language wrappers have been developed for
+ languages like C, C++, FORTRAN 95, Java, Python, etc...
+ Netlib offers exact implementations of the APIs and they are called
+ "reference" libraries. There is also some parallel implementations
+ for
+</p>
+
+<ul>
+<li>
+ <uri link="http://www.netlib.org/blas/">BLAS</uri>: FORTRAN 77 and C
+ (CBLAS) implementations of BLAS
+</li>
+<li>
+ <uri link="http://www.netlib.org/lapack/">LAPACK</uri>: FORTRAN 77 and
+ C (LAPACKE) implementations of LAPACK
+</li>
+</ul>
+
+<p>
+
+</p>
+
+<ul>
+<li>
+ <uri link="http://www.netlib.org/blacs/">BLACS</uri>: FORTRAN 77 and C
+ implementations of BLACS
+</li>
+<li>
+ <uri link="http://www.netlib.org/scalapack/">ScaLAPACK</uri>: FORTRAN 77 and
+ C implementations of PBLAS and ScaLAPACK
+</li>
+</ul>
+
+<p>
+ In addition, Gentoo provides a number of optimized implementations
+ of the above linear algebra libraries that will be described
+ below. You can switch between implementations with the
+ Gentoo's <c>eselect</c> system and the widely used <c>pkg-config</c>
+ tool.
+</p>
+
+<p>
+ It is important to note that if you require, e.g., a well performing
+ BLAS implementation, simply emerging X over Y often is not enough. Rather, you will have
+ to carefully benchmark your applications since performance may depend
+ on many factors,
+ such as hardware or network.
+ If you are simply looking for a well performing and well tested
+ implementation, the reference ebuilds will likely be your best choice.
+</p>
+
+
+</body>
+</section>
+</chapter>
+
+<chapter>
+<title>For Users</title>
+<section>
+<title>Installing</title>
+<body>
+
+<p>
+ If best possible performance is not of paramount importance for you
+ and you simply need BLAS and/or LAPACK, just emerge the virtual
+ package:
+</p>
+
+<pre caption="Installing">
+# <i>emerge lapack</i>
+</pre>
+
+<p>
+ This will install both <><> and <><> the reference packages from
+ <uri>http://www.netlib.org/</uri> . They are well tested, easy to debug
+ implementations. They should satisfy most users; if they're all you need, you're
+ done reading.
+</p>
+
+<p>
+However, if:
+</p>
+
+<ul>
+ <li>linear algebra libraries are critical for the speed of your applications</li>
+ <li>you absolutely need to build the fastest computer</li>
+ <li>you want to help Gentoo sci project to improve their packages</li>
+</ul>
+
+<p>
+... then read on, and be sure to file bugs both to Gentoo and upstream.
+</p>
+
+<p>
+ There is a number of optimized implementations of these libraries in the Portage
+ tree:
+</p>
+
+<ul>
+ <li>
+ <uri link="http://math-atlas.sourceforge.net">ATLAS</uri>: Automatically
+ Tuned Linear Algebra Software is an open-source package that empirically
+ tunes the library to the machine it is being compiled on. It provides BLAS
+ (FORTRAN 77 and C), and LAPACK implementations on various architectures.
+ </li>
+ <li>
+ <uri
+ link="http://www.tacc.utexas.edu/tacc-projects/gotoblas2/">GotoBLAS</uri>:
+ Goto BLAS provides open-source, free for academic use, hand-coded
+ machine language, processor optimized versions of the FORTRAN 77
+ and C BLAS routines. Still claims to be the fastest BLAS.
+ </li>
+ <li>
+ <uri link="http://developer.amd.com/cpu/libraries/acml/Pages/default.aspx">ACML</uri>:
+ AMD Core Math Library is a closed-source but free package containing BLAS (FORTRAN 77
+ only) and LAPACK for x86 and x86_64 architectures, but also other math tools
+ such as statistical libraries and FFTs.
+ </li>
+ <li>
+ <uri link="http://software.intel.com/en-us/articles/intel-mkl/">MKL</uri>:
+ Intel® Math Kernel Library is a closed-source but free package for
+ non-commercial use on Linux systems containing implementations of all the linear
+ algebra libraries mentioned here.
+ </li>
+</ul>
+
+<p>
+ Usually performance gain is noticeable mainly with BLAS, since LAPACK routines
+ depend on BLAS kernels.
+</p>
+
+</body>
+</section>
+
+
+<section>
+<title>Developping with the installed linear algebra libraries</title>
+<body>
+
+<p>
+ We took great care to make sure that each package provides
+ consistent pkg-config files generated by us.
+ Compiling and linking becomes straightforward:
+</p>
+
+<pre caption="Compiling and linking linear algebra libraries">
+# <i>pkg-config --libs blas</i> <comment>(To link with FORTRAN 77 BLAS library)</comment>
+# <i>pkg-config --cflags cblas</i> <comment>(To compile against C BLAS library)</comment>
+# <i>pkg-config --libs cblas</i> <comment>(To link with C BLAS library)</comment>
+# <i>pkg-config --libs scalapack</i> <comment>(To link with the ScaLAPACK library)</comment>
+</pre>
+
+<p>
+ <c>pkg-config</c> files are available for all implementations and
+ various alternatives within implementations. The default names of
+ the implementations are: blas, cblas, lapack, lapacke, blacs and
+ scalapack, and they can be chosen with <c>eselect</c>. You can also always compile or link
+ with an library not selected for the
+ More information on using <c>pkg-config</c> can be obtained with <c>man pkg-config</c>.
+</p>
+
+</body>
+</section>
+<section>
+<title>Selecting libraries</title>
+<body>
+
+<p>
+ You can switch BLAS, CBLAS and LAPACK implementations with
+ <c>eselect</c>. you can view which implementations of CBLAS
+ are available.
+</p>
+
+<pre caption="Viewing available implementations of CBLAS">
+# <i>eselect cblas list</i>
+Installed CBLAS for library directory lib64
+[1] atlas
+[2] atlas-threads
+[3] gsl
+[4] mkl-threads *
+[5] reference
+</pre>
+
+<p>
+ The implementation marked with an asterisk (*) is the currently
+ selected implementation. To switch implementations, run:
+</p>
+
+<pre caption="Switching to the threaded ATLAS implementation of BLAS">
+# <i>eselect blas set atlas-threads</i>
+</pre>
+
+<p>
+ To learn more about the <c>eselect</c> tool, visit the
+ <uri link="http://www.gentoo.org/proj/en/eselect/user-guide.xml">eselect guide</uri>
+</p>
+
+<p>
+ When selecting your linear algebra profiles try to avoid mixing
+ different implementations since we don't have any mechanism to enforce
+ reasonable profiles. However, here is a list of well performing
+ profile combinations that have been used successfully in the past:
+</p>
+<ul>
+ <li> performant on most CPUs:
+ <ul>
+ <li>blas, cblas: atlas (or atlas-threads with multi-processor)</li>
+ <li>lapack, lapacke: atlas</li>
+ </ul>
+ </li>
+ <li> performant on most CPUs:
+ <ul>
+ <li>blas, cblas: goto2 </li>
+ <li>lapack, lapacke: reference</li>
+ </ul>
+ </li>
+ <li> performant on AMD based CPUs:
+ <ul>
+ <li>blas, lapack: acml-gfortran (or acml-gfortran-openmp with
+ multi-processors) </li>
+ <li>cblas: reference</li>
+ </ul>
+ </li>
+ <li> performant on Intel based CPUs:
+ <ul>
+ <li>blas,cblas,lapack: mkl-threads</li>
+ </ul>
+ </li>
+</ul>
+
+</body>
+</section>
+
+<section>
+<title>Choosing a compiler</title>
+<body>
+
+<p>
+ All the above libraries have been tested with the GNU compiler
+ collections (gcc, gfortran).
+ There are many available C compilers and a few FORTRAN (ifort,
+ Open64) compilers on Gentoo and many other FORTRAN compilers outside
+ of Gentoo ().
+</p>
+
+<pre caption="Installing BLAS with the Intel FORTRAN compiler">
+# <i>F77=ifort FFLAGS="-O2 -mp1" emerge blas-reference</i>
+</pre>
+
+<p>
+ Depending on your hardware, a small performance gain can be noticed thanks to
+ vectorization. The <c>-mp</c> flag maintains floating-point precision, since by
+ default ifort is pretty aggressive on floating point arithmetic, and we are
+ actually compiling a math package. Try <c>man ifort</c> to see additional flags
+ to fit your hardware.
+</p>
+
+<p>
+ Some of the implementations let you specify the Intel® C compiler as
+ well. Please beware that not all libraries compile with all
+ combinations. You should receive an error during the emerge in case you have
+ chosen an incompatible combination.
+</p>
+
+<p>
+ As usual for Gentoo, there are many combinations of USE flags and
+ compilers with which you could compile a package. Unfortunately
+ switching compilers between BLAS and LAPACK might not be always
+ compatible. For example:
+</p>
+
+<pre caption="Looking for trouble combinations">
+# <i>USE=ifort emerge acml</i>
+# <i>eselect blas set acml-ifort-openmp</i>
+# <i>FC=gfortran FFLAGS="-O2" emerge lapack-reference</i>
+</pre>
+
+<p>
+ This will most likely break things or not even compile.
+</p>
+
+<p>
+ Try to be consistent in your choice. Stay with the GCC most of the time will
+ avoid you some trouble, unless you want to use the MKL, in which case the Intel
+ compilers make a good combination.
+</p>
+
+</body>
+</section>
+<section>
+<title>Documentation</title>
+<body>
+
+<p>
+ If you need BLAS or LAPACK to develop your own programs, the documentation
+ becomes pretty handy. Setting the USE="doc" flag for the corresponding BLAS or
+ LAPACK package will install man pages and quick reference sheets from the
+ <c>app-doc/blas-docs</c> and <c>app-doc/lapack-docs</c> packages. They are
+ standard and valid for all implementations. For optimized packages, the
+ USE="doc" flags will usually install extra doc in PDF or HTML format.
+</p>
+
+</body>
+</section>
+</chapter>
+
+<chapter>
+<title>For ebuild developers</title>
+<section>
+
+<section>
+<title>Packages with BLAS/LAPACK dependencies</title>
+<body>
+
+<p>
+ You need two things:
+ set [R]DEPEND to <c>virtual/<imp></c>. To build some
+ packages, you m need to use the pkg-config tool. If you are lucky, the
+ package uses autotools together with the autoconf <>AX_BLAS and <>AX_LAPACK M4
+ macros. In this case, the configuration step becomes simple. For example:
+</p>
+
+<pre caption="Sample package configuration with autotools">
+<keyword>econf</keyword> --with-blas="<var>$(pkg-config --libs blas)</var>"
+</pre>
+
+</body>
+</section>
+
+
+
+<title>Providing new implementations</title>
+<body>
+
+<p>
+ The Portage tree contains many ebuilds that depend on the
+ BLAS/CBLAS/LAPACK/BLACS/ScaLAPACK libraries. As there is more than
+ one possible implementation, the Gentoo Science Project
+ reorganized all the packages to provide <c>virtual</c>. All ebuilds using
+ should depend on this virtual package, unless it is explicitly
+ known to break with a specific implementation.
+</p>
+
+<p>
+ To work with Gentoo's configuration tools
+ <c>app-admin/eselect-{blas,cblas,lapack}</c>, and the virtual, every ebuild that
+ installs a BLAS implementation must fulfill following requirements:
+</p>
+
+<ol>
+<li>
+ The ebuild must install an eselect file for each profile it provides. The
+ libraries should link to the ones in <path>/usr/$(get_libdir)</path>
+ directories and the include files in <path>/usr/include</path>:
+ <ul>
+ <li>
+ <path>libblas.so[.0]</path> - Shared object for FORTRAN BLAS
+ applications
+ </li>
+ <li>
+ <path>libblas.a</path> - Static library for FORTRAN BLAS applications
+ </li>
+ <li>
+ <path>libcblas.so[.0]</path> - Shared object for C/C++ CBLAS applications
+ </li>
+ <li>
+ <path>libcblas.a</path> - Static library for C/C++ CBLAS applications
+ </li>
+ <li><path>cblas.h</path> - Include header for C/C++ applications</li>
+ <li>
+ <path>liblapack.so[.0]</path> - Shared object for FORTRAN LAPACK
+ applications
+ </li>
+ <li>
+ <path>liblapack.a</path> - Static library for FORTRAN LAPACK applications
+ </li>
+ </ul>
+ </li>
+ <li>
+ The ebuild must install a <path>blas.pc</path>, <path>cblas.pc</path> and/or
+ <path>lapack.pc</path> pkg-config file and therefore RDEPEND on
+ <c>dev-util/pkgconfig</c>. They should also be included in the eselect
+ files, and link to the <path>/usr/$(get_libdir)/pkgconfig</path> directory:
+ <ul>
+ <li><path>blas.pc</path> - BLAS pkg-config file</li>
+ <li><path>cblas.pc</path> - CBLAS pkg-config file</li>
+ <li><path>lapack.pc</path> - LAPACK pkg-config file</li>
+ </ul>
+ </li>
+ <li>Be included in the virtual package as a possible provider:
+ <ul>
+ <li><c>virtual/blas</c> - BLAS virtual package</li>
+ <li><c>virtual/cblas</c> - CBLAS virtual package</li>
+ <li><c>virtual/lapack</c> - LAPACK virtual package</li>
+ </ul>
+ </li>
+</ol>
+
+<p>
+ The easiest way of understanding all this is probably getting inspiration from
+ one of the available packages. Currently the Portage tree provide the following
+ virtual packages:
+</p>
+
+<table>
+<tr>
+ <th>Package name</th>
+ <th>virtual/blas</th>
+ <th>virtual/cblas</th>
+ <th>virtual/lapack</th>
+ <th>virtual/lapacke</th>
+ <th>virtual/blacs</th>
+ <th>virtual/scalapack</th>
+</tr>
+<tr>
+ <ti><c>sci-libs/acml</c></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/atlas</c></ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/gotoblas2</c></ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/blas-reference</c></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/cblas-reference</c></ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/gsl</c></ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/lapack-reference</c></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/mkl</c></ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+</tr>
+</table>
+
+</body>
+</section>
+
+</chapter>
+
+<chapter>
+<title>Benchmarks</title>
+<section>
+<body>
+
+<p>
+ If you feel inclined to write an ebuild for these, you
+ are more than welcomed to file it on our <uri
+ link="http://bugs.gentoo.org">Bugzilla</uri>.
+</p>
+
+</body>
+</section>
+</chapter>
+
+</guide>
diff --git a/docs/proj/linalg.xml b/docs/proj/linalg.xml
new file mode 100644
index 000000000..9e83460f7
--- /dev/null
+++ b/docs/proj/linalg.xml
@@ -0,0 +1,550 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE guide SYSTEM "/dtd/guide.dtd">
+<!-- $Header: $ -->
+
+<guide link="/proj/en/science/linalg.xml">
+<title>Linear Algebra on Gentoo</title>
+
+<author title="Author">
+ <mail link="bicatali@gentoo.org">Sébastien Fabbro</mail>
+</author>
+
+<abstract>
+ This guide explains the use of linear algebra libraries and focus on
+ how to use the different implementations of BLAS and LAPACK available on Gentoo.
+</abstract>
+
+<!-- The content of this document is licensed under the CC-BY-SA license -->
+<!-- See http://creativecommons.org/licenses/by-sa/2.5 -->
+<license/>
+
+<version>1.0</version>
+<date>2010-12-22</date>
+
+<chapter>
+<title>Introduction</title>
+<section>
+<body>
+
+<p>
+ There are <uri link="http://en.wikipedia.org/wiki/List_of_numerical_libraries">many</uri>
+ performant numerical libraries available.
+ The Basic Linear Algebra Subprograms (BLAS) and the Linear Algebra PACKage (LAPACK)
+ are well designed linear algebra libraries developed by the
+ High Performance Computing (HPC) community. BLAS is an API of dense
+ matrix and vectors products, while LAPACK provides routines for
+ solving systems of linear equations. Both are widely used in
+ many scientific applications and it is, therefore, important to
+ have efficient implementations available.
+</p>
+
+<p>
+ BLAS and LAPACK were originally written in FORTRAN 77. Since then, a
+ number of additional language wrappers have been developed for
+ languages like C, C++, FORTRAN 95, Java, Python, etc...
+ Netlib offers exact implementations of the APIs and they are called
+ "reference" libraries.
+</p>
+
+<ul>
+<li>
+ <uri link="http://www.netlib.org/blas/">BLAS</uri>: FORTRAN 77 and C
+ (CBLAS) implementations of BLAS
+</li>
+<li>
+ <uri link="http://www.netlib.org/lapack/">LAPACK</uri>: FORTRAN 77 and
+ C (LAPACKE) implementations of LAPACK
+</li>
+</ul>
+
+<p>
+
+</p>
+
+<ul>
+<li>
+ <uri link="http://www.netlib.org/blacs/">BLACS</uri>: FORTRAN 77 and C
+ implementations of BLACS
+</li>
+<li>
+ <uri link="http://www.netlib.org/scalapack/">ScaLAPACK</uri>: FORTRAN 77 and
+ C implementations of PBLAS and ScaLAPACK
+</li>
+</ul>
+
+<p>
+ In addition, Gentoo provides a number of optimized implementations
+ of the above linear algebra libraries that will be described
+ below. You can switch between implementations with the
+ Gentoo's <c>eselect</c> system and the widely used <c>pkg-config</c>
+ tool.
+</p>
+
+<p>
+ It is important to note that if you require, e.g., a well performing
+ BLAS implementation, simply emerging X over Y often is not enough. Rather, you will have
+ to carefully benchmark your applications since performance may depend
+ on many factors,
+ such as hardware or network.
+ If you are simply looking for a well performing and well tested
+ implementation, the reference ebuilds will likely be your best choice.
+</p>
+
+
+</body>
+</section>
+</chapter>
+
+<chapter>
+<title>For Users</title>
+<section>
+<title>Installing</title>
+<body>
+
+<p>
+ If best possible performance is not of paramount importance for you
+ and you simply need BLAS and/or LAPACK, just emerge the virtual
+ package:
+</p>
+
+<pre caption="Installing">
+# <i>emerge lapack</i>
+</pre>
+
+<p>
+ This will install both <><> and <><> the reference packages from
+ <uri>http://www.netlib.org/</uri> . They are well tested, easy to debug
+ implementations. They should satisfy most users; if they're all you need, you're
+ done reading.
+</p>
+
+<p>
+However, if:
+</p>
+
+<ul>
+ <li>linear algebra libraries are critical for the speed of your applications</li>
+ <li>you absolutely need to build the fastest computer</li>
+ <li>you want to help Gentoo sci project to improve their packages</li>
+</ul>
+
+<p>
+... then read on, and be sure to file bugs both to Gentoo and upstream.
+</p>
+
+<p>
+ There is a number of optimized implementations of these libraries in the Portage
+ tree:
+</p>
+
+<ul>
+ <li>
+ <uri link="http://math-atlas.sourceforge.net">ATLAS</uri>: Automatically
+ Tuned Linear Algebra Software is an open-source package that empirically
+ tunes the library to the machine it is being compiled on. It provides BLAS
+ (FORTRAN 77 and C), and LAPACK implementations on various architectures.
+ </li>
+ <li>
+ <uri
+ link="http://www.tacc.utexas.edu/tacc-projects/gotoblas2/">GotoBLAS</uri>:
+ Goto BLAS provides open-source, free for academic use, hand-coded
+ machine language, processor optimized versions of the FORTRAN 77
+ and C BLAS routines. Still claims to be the fastest BLAS.
+ </li>
+ <li>
+ <uri link="http://developer.amd.com/cpu/libraries/acml/Pages/default.aspx">ACML</uri>:
+ AMD Core Math Library is a closed-source but free package containing BLAS (FORTRAN 77
+ only) and LAPACK for x86 and x86_64 architectures, but also other math tools
+ such as statistical libraries and FFTs.
+ </li>
+ <li>
+ <uri link="http://software.intel.com/en-us/articles/intel-mkl/">MKL</uri>:
+ Intel® Math Kernel Library is a closed-source but free package for
+ non-commercial use on Linux systems containing implementations of all the linear
+ algebra libraries mentioned here.
+ </li>
+</ul>
+
+<p>
+ Usually performance gain is noticeable mainly with BLAS, since LAPACK routines
+ depend on BLAS kernels.
+</p>
+
+</body>
+</section>
+
+
+<section>
+<title>Developping with the installed linear algebra libraries</title>
+<body>
+
+<p>
+ We took great care to make sure that each package provides
+ consistent pkg-config files generated by us.
+ Compiling and linking becomes straightforward:
+</p>
+
+<pre caption="Compiling and linking linear algebra libraries">
+# <i>pkg-config --libs blas</i> <comment>(To link with FORTRAN 77 BLAS library)</comment>
+# <i>pkg-config --cflags cblas</i> <comment>(To compile against C BLAS library)</comment>
+# <i>pkg-config --libs cblas</i> <comment>(To link with C BLAS library)</comment>
+# <i>pkg-config --libs scalapack</i> <comment>(To link with the ScaLAPACK library)</comment>
+</pre>
+
+<p>
+ <c>pkg-config</c> files are available for all implementations and
+ various alternatives within implementations. The default names of
+ the implementations are: blas, cblas, lapack, lapacke, blacs and
+ scalapack, and they can be chosen with <c>eselect</c>. You can also always compile or link
+ with an library not selected for the
+ More information on using <c>pkg-config</c> can be obtained with <c>man pkg-config</c>.
+</p>
+
+</body>
+</section>
+<section>
+<title>Selecting libraries</title>
+<body>
+
+<p>
+ You can switch BLAS, CBLAS and LAPACK implementations with
+ <c>eselect</c>. you can view which implementations of CBLAS
+ are available.
+</p>
+
+<pre caption="Viewing available implementations of CBLAS">
+# <i>eselect cblas list</i>
+Installed CBLAS for library directory lib64
+[1] atlas
+[2] atlas-threads
+[3] gsl
+[4] mkl-threads *
+[5] reference
+</pre>
+
+<p>
+ The implementation marked with an asterisk (*) is the currently
+ selected implementation. To switch implementations, run:
+</p>
+
+<pre caption="Switching to the threaded ATLAS implementation of BLAS">
+# <i>eselect blas set atlas-threads</i>
+</pre>
+
+<p>
+ To learn more about the <c>eselect</c> tool, visit the
+ <uri link="http://www.gentoo.org/proj/en/eselect/user-guide.xml">eselect guide</uri>
+</p>
+
+<p>
+ When selecting your linear algebra profiles try to avoid mixing
+ different implementations since we don't have any mechanism to enforce
+ reasonable profiles. However, here is a list of well performing
+ profile combinations that have been used successfully in the past:
+</p>
+<ul>
+ <li> performant on most CPUs:
+ <ul>
+ <li>blas, cblas: atlas (or atlas-threads with multi-processor)</li>
+ <li>lapack, lapacke: atlas</li>
+ </ul>
+ </li>
+ <li> performant on most CPUs:
+ <ul>
+ <li>blas, cblas: goto2 </li>
+ <li>lapack, lapacke: reference</li>
+ </ul>
+ </li>
+ <li> performant on AMD based CPUs:
+ <ul>
+ <li>blas, lapack: acml-gfortran (or acml-gfortran-openmp with
+ multi-processors) </li>
+ <li>cblas: reference</li>
+ </ul>
+ </li>
+ <li> performant on Intel based CPUs:
+ <ul>
+ <li>blas,cblas,lapack: mkl-threads</li>
+ </ul>
+ </li>
+</ul>
+
+</body>
+</section>
+
+<section>
+<title>Choosing a compiler</title>
+<body>
+
+<p>
+ All the above libraries have been tested with the GNU compiler
+ collections (gcc, gfortran).
+ There are many available C compilers and a few FORTRAN (ifort,
+ Open64) compilers on Gentoo and many other FORTRAN compilers outside
+ of Gentoo ().
+</p>
+
+<pre caption="Installing BLAS with the Intel FORTRAN compiler">
+# <i>F77=ifort FFLAGS="-O2 -mp1" emerge blas-reference</i>
+</pre>
+
+<p>
+ Depending on your hardware, a small performance gain can be noticed thanks to
+ vectorization. The <c>-mp</c> flag maintains floating-point precision, since by
+ default ifort is pretty aggressive on floating point arithmetic, and we are
+ actually compiling a math package. Try <c>man ifort</c> to see additional flags
+ to fit your hardware.
+</p>
+
+<p>
+ Some of the implementations let you specify the Intel® C compiler as
+ well. Please beware that not all libraries compile with all
+ combinations. You should receive an error during the emerge in case you have
+ chosen an incompatible combination.
+</p>
+
+<p>
+ As usual for Gentoo, there are many combinations of USE flags and
+ compilers with which you could compile a package. Unfortunately
+ switching compilers between BLAS and LAPACK might not be always
+ compatible. For example:
+</p>
+
+<pre caption="Looking for trouble combinations">
+# <i>USE=ifort emerge acml</i>
+# <i>eselect blas set acml-ifort-openmp</i>
+# <i>FC=gfortran FFLAGS="-O2" emerge lapack-reference</i>
+</pre>
+
+<p>
+ This will most likely break things or not even compile.
+</p>
+
+<p>
+ Try to be consistent in your choice. Stay with the GCC most of the time will
+ avoid you some trouble, unless you want to use the MKL, in which case the Intel
+ compilers make a good combination.
+</p>
+
+</body>
+</section>
+<section>
+<title>Documentation</title>
+<body>
+
+<p>
+ If you need BLAS or LAPACK to develop your own programs, the documentation
+ becomes pretty handy. Setting the USE="doc" flag for the corresponding BLAS or
+ LAPACK package will install man pages and quick reference sheets from the
+ <c>app-doc/blas-docs</c> and <c>app-doc/lapack-docs</c> packages. They are
+ standard and valid for all implementations. For optimized packages, the
+ USE="doc" flags will usually install extra doc in PDF or HTML format.
+</p>
+
+</body>
+</section>
+</chapter>
+
+<chapter>
+<title>For ebuild developers</title>
+<section>
+
+<section>
+<title>Packages with BLAS/LAPACK dependencies</title>
+<body>
+
+<p>
+ You need two things:
+ set [R]DEPEND to <c>virtual/<imp></c>. To build some
+ packages, you m need to use the pkg-config tool. If you are lucky, the
+ package uses autotools together with the autoconf <>AX_BLAS and <>AX_LAPACK M4
+ macros. In this case, the configuration step becomes simple. For example:
+</p>
+
+<pre caption="Sample package configuration with autotools">
+<keyword>econf</keyword> --with-blas="<var>$(pkg-config --libs blas)</var>"
+</pre>
+
+</body>
+</section>
+
+
+
+<title>Providing new implementations</title>
+<body>
+
+<p>
+ The Portage tree contains many ebuilds that depend on the
+ BLAS/CBLAS/LAPACK/BLACS/ScaLAPACK libraries. As there is more than
+ one possible implementation, the Gentoo Science Project
+ reorganized all the packages to provide <c>virtual</c>. All ebuilds using
+ should depend on this virtual package, unless it is explicitly
+ known to break with a specific implementation.
+</p>
+
+<p>
+ To work with Gentoo's configuration tools
+ <c>app-admin/eselect-{blas,cblas,lapack}</c>, and the virtual, every ebuild that
+ installs a BLAS implementation must fulfill following requirements:
+</p>
+
+<ol>
+<li>
+ The ebuild must install an eselect file for each profile it provides. The
+ libraries should link to the ones in <path>/usr/$(get_libdir)</path>
+ directories and the include files in <path>/usr/include</path>:
+ <ul>
+ <li>
+ <path>libblas.so[.0]</path> - Shared object for FORTRAN BLAS
+ applications
+ </li>
+ <li>
+ <path>libblas.a</path> - Static library for FORTRAN BLAS applications
+ </li>
+ <li>
+ <path>libcblas.so[.0]</path> - Shared object for C/C++ CBLAS applications
+ </li>
+ <li>
+ <path>libcblas.a</path> - Static library for C/C++ CBLAS applications
+ </li>
+ <li><path>cblas.h</path> - Include header for C/C++ applications</li>
+ <li>
+ <path>liblapack.so[.0]</path> - Shared object for FORTRAN LAPACK
+ applications
+ </li>
+ <li>
+ <path>liblapack.a</path> - Static library for FORTRAN LAPACK applications
+ </li>
+ </ul>
+ </li>
+ <li>
+ The ebuild must install a <path>blas.pc</path>, <path>cblas.pc</path> and/or
+ <path>lapack.pc</path> pkg-config file and therefore RDEPEND on
+ <c>dev-util/pkgconfig</c>. They should also be included in the eselect
+ files, and link to the <path>/usr/$(get_libdir)/pkgconfig</path> directory:
+ <ul>
+ <li><path>blas.pc</path> - BLAS pkg-config file</li>
+ <li><path>cblas.pc</path> - CBLAS pkg-config file</li>
+ <li><path>lapack.pc</path> - LAPACK pkg-config file</li>
+ </ul>
+ </li>
+ <li>Be included in the virtual package as a possible provider:
+ <ul>
+ <li><c>virtual/blas</c> - BLAS virtual package</li>
+ <li><c>virtual/cblas</c> - CBLAS virtual package</li>
+ <li><c>virtual/lapack</c> - LAPACK virtual package</li>
+ </ul>
+ </li>
+</ol>
+
+<p>
+ The easiest way of understanding all this is probably getting inspiration from
+ one of the available packages. Currently the Portage tree provide the following
+ virtual packages:
+</p>
+
+<table>
+<tr>
+ <th>Package name</th>
+ <th>virtual/blas</th>
+ <th>virtual/cblas</th>
+ <th>virtual/lapack</th>
+ <th>virtual/lapacke</th>
+ <th>virtual/blacs</th>
+ <th>virtual/scalapack</th>
+</tr>
+<tr>
+ <ti><c>sci-libs/acml</c></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/atlas</c></ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/gotoblas2</c></ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/blas-reference</c></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/cblas-reference</c></ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/gsl</c></ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/lapack-reference</c></ti>
+ <ti></ti>
+ <ti></ti>
+ <ti>*</ti>
+ <ti></ti>
+ <ti></ti>
+ <ti></ti>
+</tr>
+<tr>
+ <ti><c>sci-libs/mkl</c></ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+ <ti>*</ti>
+</tr>
+</table>
+
+</body>
+</section>
+
+</chapter>
+
+<chapter>
+<title>Benchmarks</title>
+<section>
+<body>
+
+<p>
+ If you feel inclined to write an ebuild for these, you
+ are more than welcomed to file it on our <uri
+ link="http://bugs.gentoo.org">Bugzilla</uri>.
+</p>
+
+</body>
+</section>
+</chapter>
+
+</guide>
diff --git a/media-libs/avbin/Manifest b/media-libs/avbin/Manifest
new file mode 100644
index 000000000..ad1e60821
--- /dev/null
+++ b/media-libs/avbin/Manifest
@@ -0,0 +1,6 @@
+AUX avbin-7-ffmpeg05.patch 2258 RMD160 60e6efe5a3f8de21fcb696e8890ac74fa93441b4 SHA1 d7d9851e390547ff58c8840661ff6aa89add90dd SHA256 3a57d76fe8bf03c2403826f055ab4fe664a4bf809206ea2c9d806661dc5aea28
+AUX avbin-7-sample24.patch 716 RMD160 cbbf7d5fb66085f2727a881154d37f59115e7e79 SHA1 97429af3d55cf718e14b5df62bc6514f4b080990 SHA256 e27bf10324381a72d1583ed40802e9abcd8cfa726538b06ef6590848751b5374
+AUX avbin-7-swscale.patch 1373 RMD160 3ff64b4ccd4caec909b737f286e492f116b039d0 SHA1 36c82bd7fa7a271c16c46006442b3fdc12fe5005 SHA256 d3de05256d312d7fd1546aafbe8bdc300a72b6ff7a2204b60ef99bad029adc75
+DIST avbin-src-7.tar.gz 63378 RMD160 4f81ead95159914e1eec16cbecd6e27d4a34f6ce SHA1 46a7e5ee68601e2053587d3248e7dfe81375aee1 SHA256 3f10835e599bed8c9831ce9a04b3ff3f65866cae67ae7010c9d8a10528c651a2
+EBUILD avbin-7.ebuild 1463 RMD160 39ac47e3a9902df3de33c6e3425e0a3089822d79 SHA1 277425186100e893159acc03fc199ff0cff69d99 SHA256 e9012741a85982f8ae718c30a10711a9c3569ef8bcd61f870244154483fa133b
+MISC metadata.xml 322 RMD160 2f8aa3e8a63f8d433e70608ccb78642f787faf63 SHA1 dc9b2a3266621631cb514bfb61fe7b576f66f41e SHA256 0e951dde624ef818817a69689ab0cb6cf4d4251b3a4680a03edfa95e9997c8f8
diff --git a/media-libs/avbin/avbin-7.ebuild b/media-libs/avbin/avbin-7.ebuild
new file mode 100644
index 000000000..a7f688592
--- /dev/null
+++ b/media-libs/avbin/avbin-7.ebuild
@@ -0,0 +1,58 @@
+# Copyright 1999-2009 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+EAPI=2
+inherit eutils toolchain-funcs
+
+MYP="${PN}-src-${PV}"
+
+DESCRIPTION="Thin wrapper around FFmpeg"
+HOMEPAGE="http://code.google.com/p/avbin/"
+SRC_URI="http://avbin.googlecode.com/files/${MYP}.tar.gz"
+
+LICENSE="|| ( GPL-3 LGPL-3 )"
+SLOT="0"
+KEYWORDS="~amd64 ~x86"
+IUSE="doc"
+
+RDEPEND=">=media-video/ffmpeg-0.5"
+DEPEND="${RDEPEND}
+ dev-util/pkgconfig
+ doc? ( app-doc/doxygen )"
+
+S="${WORKDIR}/${MYP}"
+
+src_prepare() {
+ # patch for ffmpeg-0.5 to work (suppress SAMPLE_FMT_S24)
+ epatch "${FILESDIR}"/${P}-ffmpeg05.patch
+}
+
+src_compile() {
+ # ffmpeg_revision is taken from git repo at about ffmpeg-0.5 (harmless)
+ # makefile is not used
+ $(tc-getCC) ${CFLAGS} -fPIC \
+ -DAVBIN_VERSION=${PV} -Iinclude \
+ -DFFMPEG_REVISION=17762 \
+ $(pkg-config --cflags libavformat libavcodec libavutil libswscale) \
+ -c src/avbin.c -o avbin.o \
+ || die "compiling avbin failed"
+ $(tc-getCC) ${LDFLAGS} -shared -Wl,-soname,libavbin.so.${PV} \
+ avbin.o -o libavbin.so.${PV} \
+ $(pkg-config --libs libavformat libavcodec libavutil libswscale) \
+ || die "linking avbin failed"
+ ln -s libavbin.so.${PV} libavbin.so
+ if use doc; then
+ doxygen Doxyfile || die "doxygen failed"
+ fi
+}
+
+src_install() {
+ dolib.so libavbin.so* || die
+ insinto /usr/include
+ doins include/avbin.h || die
+ dodoc CHANGELOG README
+ if use doc; then
+ dohtml doc/html/* || die
+ fi
+}
diff --git a/media-libs/avbin/files/avbin-7-ffmpeg05.patch b/media-libs/avbin/files/avbin-7-ffmpeg05.patch
new file mode 100644
index 000000000..3049a49d1
--- /dev/null
+++ b/media-libs/avbin/files/avbin-7-ffmpeg05.patch
@@ -0,0 +1,66 @@
+--- src/avbin.c.orig 2009-04-02 12:09:58.000000000 +0100
++++ src/avbin.c 2009-04-02 15:07:10.000000000 +0100
+@@ -28,6 +28,7 @@
+ #include <libavformat/avformat.h>
+ #include <libavcodec/avcodec.h>
+ #include <libavutil/avutil.h>
++#include <libswscale/swscale.h>
+
+ struct _AVbinFile {
+ AVFormatContext *context;
+@@ -141,6 +142,7 @@
+ free(file->packet);
+ }
+ av_close_input_file(file->context);
++ free(file);
+ }
+
+ AVbinResult avbin_seek_file(AVbinFile *file, AVbinTimestamp timestamp)
+@@ -215,10 +217,6 @@
+ info->audio.sample_format = AVBIN_SAMPLE_FORMAT_S16;
+ info->audio.sample_bits = 16;
+ break;
+- case SAMPLE_FMT_S24:
+- info->audio.sample_format = AVBIN_SAMPLE_FORMAT_S24;
+- info->audio.sample_bits = 24;
+- break;
+ case SAMPLE_FMT_S32:
+ info->audio.sample_format = AVBIN_SAMPLE_FORMAT_S32;
+ info->audio.sample_bits = 32;
+@@ -269,6 +267,7 @@
+ if (stream->frame)
+ av_free(stream->frame);
+ avcodec_close(stream->codec_context);
++ free(stream);
+ }
+
+ int avbin_read(AVbinFile *file, AVbinPacket *packet)
+@@ -334,13 +333,21 @@
+
+ avpicture_fill(&picture_rgb, data_out, PIX_FMT_RGB24, width, height);
+
+- /* img_convert is marked deprecated in favour of swscale, don't
+- * be surprised if this stops working the next time the ffmpeg version
+- * is pushed. Example use of the new API is in ffplay.c. */
+- img_convert(&picture_rgb, PIX_FMT_RGB24,
+- (AVPicture *) stream->frame, stream->codec_context->pix_fmt,
+- width, height);
+-
++ /* use swscale instead of deprecated img_convert */
++ static struct SwsContext *img_convert_ctx = NULL;
++
++ if (img_convert_ctx == NULL) {
++ img_convert_ctx = sws_getContext(width, height,
++ stream->codec_context->pix_fmt,
++ width, height,
++ PIX_FMT_RGB24, SWS_BICUBIC, NULL, NULL, NULL);
++ }
++
++ sws_scale(img_convert_ctx, stream->frame->data, stream->frame->linesize,
++ 0, height, picture_rgb.data, picture_rgb.linesize);
++
++ /*sws_freeContext(img_convert_ctx);*/
++
+ return used;
+ }
+
diff --git a/media-libs/avbin/files/avbin-7-sample24.patch b/media-libs/avbin/files/avbin-7-sample24.patch
new file mode 100644
index 000000000..ea10d709a
--- /dev/null
+++ b/media-libs/avbin/files/avbin-7-sample24.patch
@@ -0,0 +1,15 @@
+--- src/avbin.c.orig 2009-01-15 13:49:29.000000000 -0500
++++ src/avbin.c 2009-01-15 13:50:20.000000000 -0500
+@@ -216,10 +216,12 @@ int avbin_stream_info(AVbinFile *file, i
+ info->audio.sample_format = AVBIN_SAMPLE_FORMAT_S16;
+ info->audio.sample_bits = 16;
+ break;
++#if FFMPEG_REVISION < 15124
+ case SAMPLE_FMT_S24:
+ info->audio.sample_format = AVBIN_SAMPLE_FORMAT_S24;
+ info->audio.sample_bits = 24;
+ break;
++#endif
+ case SAMPLE_FMT_S32:
+ info->audio.sample_format = AVBIN_SAMPLE_FORMAT_S32;
+ info->audio.sample_bits = 32;
diff --git a/media-libs/avbin/files/avbin-7-swscale.patch b/media-libs/avbin/files/avbin-7-swscale.patch
new file mode 100644
index 000000000..02cff3416
--- /dev/null
+++ b/media-libs/avbin/files/avbin-7-swscale.patch
@@ -0,0 +1,45 @@
+--- src/avbin.c.orig 2008-09-21 02:45:33.000000000 -0400
++++ src/avbin.c 2008-11-22 20:32:48.000000000 -0500
+@@ -28,6 +28,7 @@
+ #include <libavformat/avformat.h>
+ #include <libavcodec/avcodec.h>
+ #include <libavutil/avutil.h>
++#include <libswscale/swscale.h>
+
+ struct _AVbinFile {
+ AVFormatContext *context;
+@@ -334,9 +335,5 @@ int avbin_decode_video(AVbinStream *stre
+
+ avpicture_fill(&picture_rgb, data_out, PIX_FMT_RGB24, width, height);
+
+- /* img_convert is marked deprecated in favour of swscale, don't
+- * be surprised if this stops working the next time the ffmpeg version
+- * is pushed. Example use of the new API is in ffplay.c. */
+- img_convert(&picture_rgb, PIX_FMT_RGB24,
+- (AVPicture *) stream->frame, stream->codec_context->pix_fmt,
+- width, height);
++ static int sws_flags = SWS_BICUBIC;
++ static struct SwsContext *img_convert_ctx;
+
++ if (img_convert_ctx == NULL) {
++ img_convert_ctx = sws_getContext(width,
++ height,
++ stream->codec_context->pix_fmt,
++ width,
++ height,
++ PIX_FMT_RGB24,
++ sws_flags, NULL, NULL, NULL);
++ }
++ sws_scale(img_convert_ctx,
++ stream->frame->data,
++ stream->frame->linesize,
++ 0,
++ height,
++ picture_rgb.data,
++ picture_rgb.linesize);
++
++ sws_freeContext(img_convert_ctx);
++
+ return used;
+ }
+
diff --git a/media-libs/avbin/metadata.xml b/media-libs/avbin/metadata.xml
new file mode 100644
index 000000000..12a08660f
--- /dev/null
+++ b/media-libs/avbin/metadata.xml
@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+<herd>sci</herd>
+<longdescription lang="en">
+ AVbin is a thin wrapper around FFmpeg, providing binary compatibility
+ for applications and languages that need it.
+</longdescription>
+</pkgmetadata>
diff --git a/sci-libs/levmar/Manifest b/sci-libs/levmar/Manifest
new file mode 100644
index 000000000..aff611164
--- /dev/null
+++ b/sci-libs/levmar/Manifest
@@ -0,0 +1,2 @@
+DIST levmar-2.5.tgz 78817 RMD160 fc43206eef2969a143e3a9597b68d06eccbd14dd SHA1 46c11812689baba96f3b53cf89f0f3edb3582e61 SHA256 b70f6ac3eff30ec29150e217b137312cb84e85529815efea2c12e4eab74b9d75
+EBUILD levmar-2.5.ebuild 767 RMD160 fcfb1b860ba2f766a616e32124eb107be310e63d SHA1 1d30812114d6845ba057238102f91eb66526d5eb SHA256 331e2c710636a09d136cab0c312d8aac75b4f2f811cf315d1b0cd29aca451f38
diff --git a/sci-libs/levmar/levmar-2.5.ebuild b/sci-libs/levmar/levmar-2.5.ebuild
new file mode 100644
index 000000000..d232eb453
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5.ebuild
@@ -0,0 +1,36 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+EAPI=3
+inherit eutils
+
+DESCRIPTION="A C/C++ implementation of the Levenberg-Marquardt non-linear regression"
+HOMEPAGE="http://www.ics.forth.gr/~lourakis/levmar/"
+SRC_URI="${HOMEPAGE}/${P}.tgz"
+
+LICENSE="GPL-2"
+SLOT="0"
+KEYWORDS="~x86"
+IUSE="lapack"
+RDEPEND="lapack? ( virtual/lapack )"
+DEPEND="${RDEPEND}
+ dev-util/pkgconfig"
+
+src_prepare() {
+ sed -e "s:-O3:${CFLAGS}:" -e 's:^LAPACKLIBS_PATH.*$:LAPACKLIBS=/usr/lib:' \
+ -i Makefile
+ if ! use lapack; then
+ sed -e 's:^LAPACKFLAG:#LAPACKFLAG:' \
+ -e 's:^LAPACKLIBS:#LAPACKLIBS:' \
+ -i Makefile
+ fi
+
+}
+
+src_install() {
+ dolib liblevmar.a
+ insinto /usr/include
+ doins lm.h
+ dodoc README.txt
+}
diff --git a/sci-libs/levmar/levmar-2.5/Axb.c b/sci-libs/levmar/levmar-2.5/Axb.c
new file mode 100644
index 000000000..7fce7c276
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/Axb.c
@@ -0,0 +1,74 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Solution of linear systems involved in the Levenberg - Marquardt
+// minimization algorithm
+// Copyright (C) 2004 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/********************************************************************************
+ * LAPACK-based implementations for various linear system solvers. The same core
+ * code is used with appropriate #defines to derive single and double precision
+ * solver versions, see also Axb_core.c
+ ********************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include "levmar.h"
+#include "misc.h"
+
+#if !defined(LM_DBL_PREC) && !defined(LM_SNGL_PREC)
+#error At least one of LM_DBL_PREC, LM_SNGL_PREC should be defined!
+#endif
+
+
+#ifdef LM_DBL_PREC
+/* double precision definitions */
+#define LM_REAL double
+#define LM_PREFIX d
+#define LM_CNST(x) (x)
+#ifndef HAVE_LAPACK
+#include <float.h>
+#define LM_REAL_EPSILON DBL_EPSILON
+#endif
+
+#include "Axb_core.c"
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_CNST
+#undef LM_REAL_EPSILON
+#endif /* LM_DBL_PREC */
+
+#ifdef LM_SNGL_PREC
+/* single precision (float) definitions */
+#define LM_REAL float
+#define LM_PREFIX s
+#define __SUBCNST(x) x##F
+#define LM_CNST(x) __SUBCNST(x) // force substitution
+#ifndef HAVE_LAPACK
+#define LM_REAL_EPSILON FLT_EPSILON
+#endif
+
+#include "Axb_core.c"
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef __SUBCNST
+#undef LM_CNST
+#undef LM_REAL_EPSILON
+#endif /* LM_SNGL_PREC */
diff --git a/sci-libs/levmar/levmar-2.5/Axb.o b/sci-libs/levmar/levmar-2.5/Axb.o
new file mode 100644
index 000000000..51384256f
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/Axb.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/Axb_core.c b/sci-libs/levmar/levmar-2.5/Axb_core.c
new file mode 100644
index 000000000..edeeac3b6
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/Axb_core.c
@@ -0,0 +1,1144 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Solution of linear systems involved in the Levenberg - Marquardt
+// minimization algorithm
+// Copyright (C) 2004 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+
+/* Solvers for the linear systems Ax=b. Solvers should NOT modify their A & B arguments! */
+
+
+#ifndef LM_REAL // not included by Axb.c
+#error This file should not be compiled directly!
+#endif
+
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+#define __STATIC__ static
+#else
+#define __STATIC__ // empty
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+#ifdef HAVE_LAPACK
+
+/* prototypes of LAPACK routines */
+
+#define GEQRF LM_MK_LAPACK_NAME(geqrf)
+#define ORGQR LM_MK_LAPACK_NAME(orgqr)
+#define TRTRS LM_MK_LAPACK_NAME(trtrs)
+#define POTF2 LM_MK_LAPACK_NAME(potf2)
+#define POTRF LM_MK_LAPACK_NAME(potrf)
+#define POTRS LM_MK_LAPACK_NAME(potrs)
+#define GETRF LM_MK_LAPACK_NAME(getrf)
+#define GETRS LM_MK_LAPACK_NAME(getrs)
+#define GESVD LM_MK_LAPACK_NAME(gesvd)
+#define GESDD LM_MK_LAPACK_NAME(gesdd)
+#define SYTRF LM_MK_LAPACK_NAME(sytrf)
+#define SYTRS LM_MK_LAPACK_NAME(sytrs)
+
+/* QR decomposition */
+extern int GEQRF(int *m, int *n, LM_REAL *a, int *lda, LM_REAL *tau, LM_REAL *work, int *lwork, int *info);
+extern int ORGQR(int *m, int *n, int *k, LM_REAL *a, int *lda, LM_REAL *tau, LM_REAL *work, int *lwork, int *info);
+
+/* solution of triangular systems */
+extern int TRTRS(char *uplo, char *trans, char *diag, int *n, int *nrhs, LM_REAL *a, int *lda, LM_REAL *b, int *ldb, int *info);
+
+/* Cholesky decomposition and systems solution */
+extern int POTF2(char *uplo, int *n, LM_REAL *a, int *lda, int *info);
+extern int POTRF(char *uplo, int *n, LM_REAL *a, int *lda, int *info); /* block version of dpotf2 */
+extern int POTRS(char *uplo, int *n, int *nrhs, LM_REAL *a, int *lda, LM_REAL *b, int *ldb, int *info);
+
+/* LU decomposition and systems solution */
+extern int GETRF(int *m, int *n, LM_REAL *a, int *lda, int *ipiv, int *info);
+extern int GETRS(char *trans, int *n, int *nrhs, LM_REAL *a, int *lda, int *ipiv, LM_REAL *b, int *ldb, int *info);
+
+/* Singular Value Decomposition (SVD) */
+extern int GESVD(char *jobu, char *jobvt, int *m, int *n, LM_REAL *a, int *lda, LM_REAL *s, LM_REAL *u, int *ldu,
+ LM_REAL *vt, int *ldvt, LM_REAL *work, int *lwork, int *info);
+
+/* lapack 3.0 new SVD routine, faster than xgesvd().
+ * In case that your version of LAPACK does not include them, use the above two older routines
+ */
+extern int GESDD(char *jobz, int *m, int *n, LM_REAL *a, int *lda, LM_REAL *s, LM_REAL *u, int *ldu, LM_REAL *vt, int *ldvt,
+ LM_REAL *work, int *lwork, int *iwork, int *info);
+
+/* LDLt/UDUt factorization and systems solution */
+extern int SYTRF(char *uplo, int *n, LM_REAL *a, int *lda, int *ipiv, LM_REAL *work, int *lwork, int *info);
+extern int SYTRS(char *uplo, int *n, int *nrhs, LM_REAL *a, int *lda, int *ipiv, LM_REAL *b, int *ldb, int *info);
+
+/* precision-specific definitions */
+#define AX_EQ_B_QR LM_ADD_PREFIX(Ax_eq_b_QR)
+#define AX_EQ_B_QRLS LM_ADD_PREFIX(Ax_eq_b_QRLS)
+#define AX_EQ_B_CHOL LM_ADD_PREFIX(Ax_eq_b_Chol)
+#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU)
+#define AX_EQ_B_SVD LM_ADD_PREFIX(Ax_eq_b_SVD)
+#define AX_EQ_B_BK LM_ADD_PREFIX(Ax_eq_b_BK)
+
+/*
+ * This function returns the solution of Ax = b
+ *
+ * The function is based on QR decomposition with explicit computation of Q:
+ * If A=Q R with Q orthogonal and R upper triangular, the linear system becomes
+ * Q R x = b or R x = Q^T b.
+ * The last equation can be solved directly.
+ *
+ * A is mxm, b is mx1
+ *
+ * The function returns 0 in case of error, 1 if successful
+ *
+ * This function is often called repetitively to solve problems of identical
+ * dimensions. To avoid repetitive malloc's and free's, allocated memory is
+ * retained between calls and free'd-malloc'ed when not of the appropriate size.
+ * A call with NULL as the first argument forces this memory to be released.
+ */
+int AX_EQ_B_QR(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)
+{
+__STATIC__ LM_REAL *buf=NULL;
+__STATIC__ int buf_sz=0;
+
+static int nb=0; /* no __STATIC__ decl. here! */
+
+LM_REAL *a, *tau, *r, *work;
+int a_sz, tau_sz, r_sz, tot_sz;
+register int i, j;
+int info, worksz, nrhs=1;
+register LM_REAL sum;
+
+ if(!A)
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ {
+ if(buf) free(buf);
+ buf=NULL;
+ buf_sz=0;
+
+ return 1;
+ }
+#else
+ return 1; /* NOP */
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ /* calculate required memory size */
+ a_sz=m*m;
+ tau_sz=m;
+ r_sz=m*m; /* only the upper triangular part really needed */
+ if(!nb){
+ LM_REAL tmp;
+
+ worksz=-1; // workspace query; optimal size is returned in tmp
+ GEQRF((int *)&m, (int *)&m, NULL, (int *)&m, NULL, (LM_REAL *)&tmp, (int *)&worksz, (int *)&info);
+ nb=((int)tmp)/m; // optimal worksize is m*nb
+ }
+ worksz=nb*m;
+ tot_sz=a_sz + tau_sz + r_sz + worksz;
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(tot_sz>buf_sz){ /* insufficient memory, allocate a "big" memory chunk at once */
+ if(buf) free(buf); /* free previously allocated memory */
+
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz*sizeof(LM_REAL));
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_QR) "() failed!\n");
+ exit(1);
+ }
+ }
+#else
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz*sizeof(LM_REAL));
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_QR) "() failed!\n");
+ exit(1);
+ }
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ a=buf;
+ tau=a+a_sz;
+ r=tau+tau_sz;
+ work=r+r_sz;
+
+ /* store A (column major!) into a */
+ for(i=0; i<m; i++)
+ for(j=0; j<m; j++)
+ a[i+j*m]=A[i*m+j];
+
+ /* QR decomposition of A */
+ GEQRF((int *)&m, (int *)&m, a, (int *)&m, tau, work, (int *)&worksz, (int *)&info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", GEQRF) " in ", AX_EQ_B_QR) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT(RCAT("Unknown LAPACK error %d for ", GEQRF) " in ", AX_EQ_B_QR) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* R is stored in the upper triangular part of a; copy it in r so that ORGQR() below won't destroy it */
+ for(i=0; i<r_sz; i++)
+ r[i]=a[i];
+
+ /* compute Q using the elementary reflectors computed by the above decomposition */
+ ORGQR((int *)&m, (int *)&m, (int *)&m, a, (int *)&m, tau, work, (int *)&worksz, (int *)&info);
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", ORGQR) " in ", AX_EQ_B_QR) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT("Unknown LAPACK error (%d) in ", AX_EQ_B_QR) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* Q is now in a; compute Q^T b in x */
+ for(i=0; i<m; i++){
+ for(j=0, sum=0.0; j<m; j++)
+ sum+=a[i*m+j]*B[j];
+ x[i]=sum;
+ }
+
+ /* solve the linear system R x = Q^t b */
+ TRTRS("U", "N", "N", (int *)&m, (int *)&nrhs, r, (int *)&m, x, (int *)&m, &info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", TRTRS) " in ", AX_EQ_B_QR) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT("LAPACK error: the %d-th diagonal element of A is zero (singular matrix) in ", AX_EQ_B_QR) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 1;
+}
+
+/*
+ * This function returns the solution of min_x ||Ax - b||
+ *
+ * || . || is the second order (i.e. L2) norm. This is a least squares technique that
+ * is based on QR decomposition:
+ * If A=Q R with Q orthogonal and R upper triangular, the normal equations become
+ * (A^T A) x = A^T b or (R^T Q^T Q R) x = A^T b or (R^T R) x = A^T b.
+ * This amounts to solving R^T y = A^T b for y and then R x = y for x
+ * Note that Q does not need to be explicitly computed
+ *
+ * A is mxn, b is mx1
+ *
+ * The function returns 0 in case of error, 1 if successful
+ *
+ * This function is often called repetitively to solve problems of identical
+ * dimensions. To avoid repetitive malloc's and free's, allocated memory is
+ * retained between calls and free'd-malloc'ed when not of the appropriate size.
+ * A call with NULL as the first argument forces this memory to be released.
+ */
+int AX_EQ_B_QRLS(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m, int n)
+{
+__STATIC__ LM_REAL *buf=NULL;
+__STATIC__ int buf_sz=0;
+
+static int nb=0; /* no __STATIC__ decl. here! */
+
+LM_REAL *a, *tau, *r, *work;
+int a_sz, tau_sz, r_sz, tot_sz;
+register int i, j;
+int info, worksz, nrhs=1;
+register LM_REAL sum;
+
+ if(!A)
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ {
+ if(buf) free(buf);
+ buf=NULL;
+ buf_sz=0;
+
+ return 1;
+ }
+#else
+ return 1; /* NOP */
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ if(m<n){
+ fprintf(stderr, RCAT("Normal equations require that the number of rows is greater than number of columns in ", AX_EQ_B_QRLS) "() [%d x %d]! -- try transposing\n", m, n);
+ exit(1);
+ }
+
+ /* calculate required memory size */
+ a_sz=m*n;
+ tau_sz=n;
+ r_sz=n*n;
+ if(!nb){
+ LM_REAL tmp;
+
+ worksz=-1; // workspace query; optimal size is returned in tmp
+ GEQRF((int *)&m, (int *)&m, NULL, (int *)&m, NULL, (LM_REAL *)&tmp, (int *)&worksz, (int *)&info);
+ nb=((int)tmp)/m; // optimal worksize is m*nb
+ }
+ worksz=nb*m;
+ tot_sz=a_sz + tau_sz + r_sz + worksz;
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(tot_sz>buf_sz){ /* insufficient memory, allocate a "big" memory chunk at once */
+ if(buf) free(buf); /* free previously allocated memory */
+
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz*sizeof(LM_REAL));
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_QRLS) "() failed!\n");
+ exit(1);
+ }
+ }
+#else
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz*sizeof(LM_REAL));
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_QRLS) "() failed!\n");
+ exit(1);
+ }
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ a=buf;
+ tau=a+a_sz;
+ r=tau+tau_sz;
+ work=r+r_sz;
+
+ /* store A (column major!) into a */
+ for(i=0; i<m; i++)
+ for(j=0; j<n; j++)
+ a[i+j*m]=A[i*n+j];
+
+ /* compute A^T b in x */
+ for(i=0; i<n; i++){
+ for(j=0, sum=0.0; j<m; j++)
+ sum+=A[j*n+i]*B[j];
+ x[i]=sum;
+ }
+
+ /* QR decomposition of A */
+ GEQRF((int *)&m, (int *)&n, a, (int *)&m, tau, work, (int *)&worksz, (int *)&info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", GEQRF) " in ", AX_EQ_B_QRLS) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT(RCAT("Unknown LAPACK error %d for ", GEQRF) " in ", AX_EQ_B_QRLS) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* R is stored in the upper triangular part of a. Note that a is mxn while r nxn */
+ for(j=0; j<n; j++){
+ for(i=0; i<=j; i++)
+ r[i+j*n]=a[i+j*m];
+
+ /* lower part is zero */
+ for(i=j+1; i<n; i++)
+ r[i+j*n]=0.0;
+ }
+
+ /* solve the linear system R^T y = A^t b */
+ TRTRS("U", "T", "N", (int *)&n, (int *)&nrhs, r, (int *)&n, x, (int *)&n, &info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", TRTRS) " in ", AX_EQ_B_QRLS) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT("LAPACK error: the %d-th diagonal element of A is zero (singular matrix) in ", AX_EQ_B_QRLS) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* solve the linear system R x = y */
+ TRTRS("U", "N", "N", (int *)&n, (int *)&nrhs, r, (int *)&n, x, (int *)&n, &info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", TRTRS) " in ", AX_EQ_B_QRLS) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT("LAPACK error: the %d-th diagonal element of A is zero (singular matrix) in ", AX_EQ_B_QRLS) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 1;
+}
+
+/*
+ * This function returns the solution of Ax=b
+ *
+ * The function assumes that A is symmetric & postive definite and employs
+ * the Cholesky decomposition:
+ * If A=U^T U with U upper triangular, the system to be solved becomes
+ * (U^T U) x = b
+ * This amount to solving U^T y = b for y and then U x = y for x
+ *
+ * A is mxm, b is mx1
+ *
+ * The function returns 0 in case of error, 1 if successful
+ *
+ * This function is often called repetitively to solve problems of identical
+ * dimensions. To avoid repetitive malloc's and free's, allocated memory is
+ * retained between calls and free'd-malloc'ed when not of the appropriate size.
+ * A call with NULL as the first argument forces this memory to be released.
+ */
+int AX_EQ_B_CHOL(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)
+{
+__STATIC__ LM_REAL *buf=NULL;
+__STATIC__ int buf_sz=0;
+
+LM_REAL *a;
+int a_sz, tot_sz;
+register int i;
+int info, nrhs=1;
+
+ if(!A)
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ {
+ if(buf) free(buf);
+ buf=NULL;
+ buf_sz=0;
+
+ return 1;
+ }
+#else
+ return 1; /* NOP */
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ /* calculate required memory size */
+ a_sz=m*m;
+ tot_sz=a_sz;
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(tot_sz>buf_sz){ /* insufficient memory, allocate a "big" memory chunk at once */
+ if(buf) free(buf); /* free previously allocated memory */
+
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz*sizeof(LM_REAL));
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_CHOL) "() failed!\n");
+ exit(1);
+ }
+ }
+#else
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz*sizeof(LM_REAL));
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_CHOL) "() failed!\n");
+ exit(1);
+ }
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ a=buf;
+
+ /* store A into a and B into x. A is assumed symmetric,
+ * hence no transposition is needed
+ */
+ for(i=0; i<m; i++){
+ a[i]=A[i];
+ x[i]=B[i];
+ }
+ for(i=m; i<m*m; i++)
+ a[i]=A[i];
+
+ /* Cholesky decomposition of A */
+ //POTF2("U", (int *)&m, a, (int *)&m, (int *)&info);
+ POTRF("U", (int *)&m, a, (int *)&m, (int *)&info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT(RCAT("LAPACK error: illegal value for argument %d of ", POTF2) "/", POTRF) " in ",
+ AX_EQ_B_CHOL) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT(RCAT(RCAT("LAPACK error: the leading minor of order %d is not positive definite,\nthe factorization could not be completed for ", POTF2) "/", POTRF) " in ", AX_EQ_B_CHOL) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* solve using the computed Cholesky in one lapack call */
+ POTRS("U", (int *)&m, (int *)&nrhs, a, (int *)&m, x, (int *)&m, &info);
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", POTRS) " in ", AX_EQ_B_CHOL) "()\n", -info);
+ exit(1);
+ }
+
+#if 0
+ /* alternative: solve the linear system U^T y = b ... */
+ TRTRS("U", "T", "N", (int *)&m, (int *)&nrhs, a, (int *)&m, x, (int *)&m, &info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", TRTRS) " in ", AX_EQ_B_CHOL) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT("LAPACK error: the %d-th diagonal element of A is zero (singular matrix) in ", AX_EQ_B_CHOL) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* ... solve the linear system U x = y */
+ TRTRS("U", "N", "N", (int *)&m, (int *)&nrhs, a, (int *)&m, x, (int *)&m, &info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", TRTRS) "in ", AX_EQ_B_CHOL) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT("LAPACK error: the %d-th diagonal element of A is zero (singular matrix) in ", AX_EQ_B_CHOL) "()\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+#endif /* 0 */
+
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 1;
+}
+
+/*
+ * This function returns the solution of Ax = b
+ *
+ * The function employs LU decomposition:
+ * If A=L U with L lower and U upper triangular, then the original system
+ * amounts to solving
+ * L y = b, U x = y
+ *
+ * A is mxm, b is mx1
+ *
+ * The function returns 0 in case of error, 1 if successful
+ *
+ * This function is often called repetitively to solve problems of identical
+ * dimensions. To avoid repetitive malloc's and free's, allocated memory is
+ * retained between calls and free'd-malloc'ed when not of the appropriate size.
+ * A call with NULL as the first argument forces this memory to be released.
+ */
+int AX_EQ_B_LU(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)
+{
+__STATIC__ LM_REAL *buf=NULL;
+__STATIC__ int buf_sz=0;
+
+int a_sz, ipiv_sz, tot_sz;
+register int i, j;
+int info, *ipiv, nrhs=1;
+LM_REAL *a;
+
+ if(!A)
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ {
+ if(buf) free(buf);
+ buf=NULL;
+ buf_sz=0;
+
+ return 1;
+ }
+#else
+ return 1; /* NOP */
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ /* calculate required memory size */
+ ipiv_sz=m;
+ a_sz=m*m;
+ tot_sz=a_sz*sizeof(LM_REAL) + ipiv_sz*sizeof(int); /* should be arranged in that order for proper doubles alignment */
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(tot_sz>buf_sz){ /* insufficient memory, allocate a "big" memory chunk at once */
+ if(buf) free(buf); /* free previously allocated memory */
+
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_LU) "() failed!\n");
+ exit(1);
+ }
+ }
+#else
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_LU) "() failed!\n");
+ exit(1);
+ }
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ a=buf;
+ ipiv=(int *)(a+a_sz);
+
+ /* store A (column major!) into a and B into x */
+ for(i=0; i<m; i++){
+ for(j=0; j<m; j++)
+ a[i+j*m]=A[i*m+j];
+
+ x[i]=B[i];
+ }
+
+ /* LU decomposition for A */
+ GETRF((int *)&m, (int *)&m, a, (int *)&m, ipiv, (int *)&info);
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("argument %d of ", GETRF) " illegal in ", AX_EQ_B_LU) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT(RCAT("singular matrix A for ", GETRF) " in ", AX_EQ_B_LU) "()\n");
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* solve the system with the computed LU */
+ GETRS("N", (int *)&m, (int *)&nrhs, a, (int *)&m, ipiv, x, (int *)&m, (int *)&info);
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("argument %d of ", GETRS) " illegal in ", AX_EQ_B_LU) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT(RCAT("unknown error for ", GETRS) " in ", AX_EQ_B_LU) "()\n");
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 1;
+}
+
+/*
+ * This function returns the solution of Ax = b
+ *
+ * The function is based on SVD decomposition:
+ * If A=U D V^T with U, V orthogonal and D diagonal, the linear system becomes
+ * (U D V^T) x = b or x=V D^{-1} U^T b
+ * Note that V D^{-1} U^T is the pseudoinverse A^+
+ *
+ * A is mxm, b is mx1.
+ *
+ * The function returns 0 in case of error, 1 if successful
+ *
+ * This function is often called repetitively to solve problems of identical
+ * dimensions. To avoid repetitive malloc's and free's, allocated memory is
+ * retained between calls and free'd-malloc'ed when not of the appropriate size.
+ * A call with NULL as the first argument forces this memory to be released.
+ */
+int AX_EQ_B_SVD(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)
+{
+__STATIC__ LM_REAL *buf=NULL;
+__STATIC__ int buf_sz=0;
+static LM_REAL eps=LM_CNST(-1.0);
+
+register int i, j;
+LM_REAL *a, *u, *s, *vt, *work;
+int a_sz, u_sz, s_sz, vt_sz, tot_sz;
+LM_REAL thresh, one_over_denom;
+register LM_REAL sum;
+int info, rank, worksz, *iwork, iworksz;
+
+ if(!A)
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ {
+ if(buf) free(buf);
+ buf=NULL;
+ buf_sz=0;
+
+ return 1;
+ }
+#else
+ return 1; /* NOP */
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ /* calculate required memory size */
+#if 1 /* use optimal size */
+ worksz=-1; // workspace query. Keep in mind that GESDD requires more memory than GESVD
+ /* note that optimal work size is returned in thresh */
+ GESVD("A", "A", (int *)&m, (int *)&m, NULL, (int *)&m, NULL, NULL, (int *)&m, NULL, (int *)&m, (LM_REAL *)&thresh, (int *)&worksz, &info);
+ //GESDD("A", (int *)&m, (int *)&m, NULL, (int *)&m, NULL, NULL, (int *)&m, NULL, (int *)&m, (LM_REAL *)&thresh, (int *)&worksz, NULL, &info);
+ worksz=(int)thresh;
+#else /* use minimum size */
+ worksz=5*m; // min worksize for GESVD
+ //worksz=m*(7*m+4); // min worksize for GESDD
+#endif
+ iworksz=8*m;
+ a_sz=m*m;
+ u_sz=m*m; s_sz=m; vt_sz=m*m;
+
+ tot_sz=(a_sz + u_sz + s_sz + vt_sz + worksz)*sizeof(LM_REAL) + iworksz*sizeof(int); /* should be arranged in that order for proper doubles alignment */
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(tot_sz>buf_sz){ /* insufficient memory, allocate a "big" memory chunk at once */
+ if(buf) free(buf); /* free previously allocated memory */
+
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_SVD) "() failed!\n");
+ exit(1);
+ }
+ }
+#else
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_SVD) "() failed!\n");
+ exit(1);
+ }
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ a=buf;
+ u=a+a_sz;
+ s=u+u_sz;
+ vt=s+s_sz;
+ work=vt+vt_sz;
+ iwork=(int *)(work+worksz);
+
+ /* store A (column major!) into a */
+ for(i=0; i<m; i++)
+ for(j=0; j<m; j++)
+ a[i+j*m]=A[i*m+j];
+
+ /* SVD decomposition of A */
+ GESVD("A", "A", (int *)&m, (int *)&m, a, (int *)&m, s, u, (int *)&m, vt, (int *)&m, work, (int *)&worksz, &info);
+ //GESDD("A", (int *)&m, (int *)&m, a, (int *)&m, s, u, (int *)&m, vt, (int *)&m, work, (int *)&worksz, iwork, &info);
+
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT(RCAT("LAPACK error: illegal value for argument %d of ", GESVD), "/" GESDD) " in ", AX_EQ_B_SVD) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT("LAPACK error: dgesdd (dbdsdc)/dgesvd (dbdsqr) failed to converge in ", AX_EQ_B_SVD) "() [info=%d]\n", info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ if(eps<0.0){
+ LM_REAL aux;
+
+ /* compute machine epsilon */
+ for(eps=LM_CNST(1.0); aux=eps+LM_CNST(1.0), aux-LM_CNST(1.0)>0.0; eps*=LM_CNST(0.5))
+ ;
+ eps*=LM_CNST(2.0);
+ }
+
+ /* compute the pseudoinverse in a */
+ for(i=0; i<a_sz; i++) a[i]=0.0; /* initialize to zero */
+ for(rank=0, thresh=eps*s[0]; rank<m && s[rank]>thresh; rank++){
+ one_over_denom=LM_CNST(1.0)/s[rank];
+
+ for(j=0; j<m; j++)
+ for(i=0; i<m; i++)
+ a[i*m+j]+=vt[rank+i*m]*u[j+rank*m]*one_over_denom;
+ }
+
+ /* compute A^+ b in x */
+ for(i=0; i<m; i++){
+ for(j=0, sum=0.0; j<m; j++)
+ sum+=a[i*m+j]*B[j];
+ x[i]=sum;
+ }
+
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 1;
+}
+
+/*
+ * This function returns the solution of Ax = b for a real symmetric matrix A
+ *
+ * The function is based on UDUT factorization with the pivoting
+ * strategy of Bunch and Kaufman:
+ * A is factored as U*D*U^T where U is upper triangular and
+ * D symmetric and block diagonal (aka spectral decomposition,
+ * Banachiewicz factorization, modified Cholesky factorization)
+ *
+ * A is mxm, b is mx1.
+ *
+ * The function returns 0 in case of error, 1 if successfull
+ *
+ * This function is often called repetitively to solve problems of identical
+ * dimensions. To avoid repetitive malloc's and free's, allocated memory is
+ * retained between calls and free'd-malloc'ed when not of the appropriate size.
+ * A call with NULL as the first argument forces this memory to be released.
+ */
+int AX_EQ_B_BK(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)
+{
+__STATIC__ LM_REAL *buf=NULL;
+__STATIC__ int buf_sz=0, nb=0;
+
+LM_REAL *a, *work;
+int a_sz, ipiv_sz, work_sz, tot_sz;
+register int i, j;
+int info, *ipiv, nrhs=1;
+
+ if(!A)
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ {
+ if(buf) free(buf);
+ buf=NULL;
+ buf_sz=0;
+
+ return 1;
+ }
+#else
+ return 1; /* NOP */
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ /* calculate required memory size */
+ ipiv_sz=m;
+ a_sz=m*m;
+ if(!nb){
+ LM_REAL tmp;
+
+ work_sz=-1; // workspace query; optimal size is returned in tmp
+ SYTRF("U", (int *)&m, NULL, (int *)&m, NULL, (LM_REAL *)&tmp, (int *)&work_sz, (int *)&info);
+ nb=((int)tmp)/m; // optimal worksize is m*nb
+ }
+ work_sz=(nb!=-1)? nb*m : 1;
+ tot_sz=(a_sz + work_sz)*sizeof(LM_REAL) + ipiv_sz*sizeof(int); /* should be arranged in that order for proper doubles alignment */
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(tot_sz>buf_sz){ /* insufficient memory, allocate a "big" memory chunk at once */
+ if(buf) free(buf); /* free previously allocated memory */
+
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_BK) "() failed!\n");
+ exit(1);
+ }
+ }
+#else
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_BK) "() failed!\n");
+ exit(1);
+ }
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ a=buf;
+ work=a+a_sz;
+ ipiv=(int *)(work+work_sz);
+
+ /* store A into a and B into x; A is assumed to be symmetric, hence
+ * the column and row major order representations are the same
+ */
+ for(i=0; i<m; ++i){
+ a[i]=A[i];
+ x[i]=B[i];
+ }
+ for(j=m*m; i<j; ++i) // copy remaining rows; note that i is not re-initialized
+ a[i]=A[i];
+
+ /* UDUt factorization for A */
+ SYTRF("U", (int *)&m, a, (int *)&m, ipiv, work, (int *)&work_sz, (int *)&info);
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", SYTRF) " in ", AX_EQ_B_BK) "()\n", -info);
+ exit(1);
+ }
+ else{
+ fprintf(stderr, RCAT(RCAT("LAPACK error: singular block diagonal matrix D for", SYTRF) " in ", AX_EQ_B_BK)"() [D(%d, %d) is zero]\n", info, info);
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ }
+
+ /* solve the system with the computed factorization */
+ SYTRS("U", (int *)&m, (int *)&nrhs, a, (int *)&m, ipiv, x, (int *)&m, (int *)&info);
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", SYTRS) " in ", AX_EQ_B_BK) "()\n", -info);
+ exit(1);
+ }
+
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 1;
+}
+
+/* undefine all. IT MUST REMAIN IN THIS POSITION IN FILE */
+#undef AX_EQ_B_QR
+#undef AX_EQ_B_QRLS
+#undef AX_EQ_B_CHOL
+#undef AX_EQ_B_LU
+#undef AX_EQ_B_SVD
+#undef AX_EQ_B_BK
+
+#undef GEQRF
+#undef ORGQR
+#undef TRTRS
+#undef POTF2
+#undef POTRF
+#undef POTRS
+#undef GETRF
+#undef GETRS
+#undef GESVD
+#undef GESDD
+#undef SYTRF
+#undef SYTRS
+
+#else // no LAPACK
+
+/* precision-specific definitions */
+#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU_noLapack)
+
+/*
+ * This function returns the solution of Ax = b
+ *
+ * The function employs LU decomposition followed by forward/back substitution (see
+ * also the LAPACK-based LU solver above)
+ *
+ * A is mxm, b is mx1
+ *
+ * The function returns 0 in case of error, 1 if successful
+ *
+ * This function is often called repetitively to solve problems of identical
+ * dimensions. To avoid repetitive malloc's and free's, allocated memory is
+ * retained between calls and free'd-malloc'ed when not of the appropriate size.
+ * A call with NULL as the first argument forces this memory to be released.
+ */
+int AX_EQ_B_LU(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)
+{
+__STATIC__ void *buf=NULL;
+__STATIC__ int buf_sz=0;
+
+register int i, j, k;
+int *idx, maxi=-1, idx_sz, a_sz, work_sz, tot_sz;
+LM_REAL *a, *work, max, sum, tmp;
+
+ if(!A)
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ {
+ if(buf) free(buf);
+ buf=NULL;
+ buf_sz=0;
+
+ return 1;
+ }
+#else
+ return 1; /* NOP */
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ /* calculate required memory size */
+ idx_sz=m;
+ a_sz=m*m;
+ work_sz=m;
+ tot_sz=(a_sz+work_sz)*sizeof(LM_REAL) + idx_sz*sizeof(int); /* should be arranged in that order for proper doubles alignment */
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(tot_sz>buf_sz){ /* insufficient memory, allocate a "big" memory chunk at once */
+ if(buf) free(buf); /* free previously allocated memory */
+
+ buf_sz=tot_sz;
+ buf=(void *)malloc(tot_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_LU) "() failed!\n");
+ exit(1);
+ }
+ }
+#else
+ buf_sz=tot_sz;
+ buf=(void *)malloc(tot_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", AX_EQ_B_LU) "() failed!\n");
+ exit(1);
+ }
+#endif /* LINSOLVERS_RETAIN_MEMORY */
+
+ a=buf;
+ work=a+a_sz;
+ idx=(int *)(work+work_sz);
+
+ /* avoid destroying A, B by copying them to a, x resp. */
+ for(i=0; i<m; ++i){ // B & 1st row of A
+ a[i]=A[i];
+ x[i]=B[i];
+ }
+ for( ; i<a_sz; ++i) a[i]=A[i]; // copy A's remaining rows
+ /****
+ for(i=0; i<m; ++i){
+ for(j=0; j<m; ++j)
+ a[i*m+j]=A[i*m+j];
+ x[i]=B[i];
+ }
+ ****/
+
+ /* compute the LU decomposition of a row permutation of matrix a; the permutation itself is saved in idx[] */
+ for(i=0; i<m; ++i){
+ max=0.0;
+ for(j=0; j<m; ++j)
+ if((tmp=FABS(a[i*m+j]))>max)
+ max=tmp;
+ if(max==0.0){
+ fprintf(stderr, RCAT("Singular matrix A in ", AX_EQ_B_LU) "()!\n");
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 0;
+ }
+ work[i]=LM_CNST(1.0)/max;
+ }
+
+ for(j=0; j<m; ++j){
+ for(i=0; i<j; ++i){
+ sum=a[i*m+j];
+ for(k=0; k<i; ++k)
+ sum-=a[i*m+k]*a[k*m+j];
+ a[i*m+j]=sum;
+ }
+ max=0.0;
+ for(i=j; i<m; ++i){
+ sum=a[i*m+j];
+ for(k=0; k<j; ++k)
+ sum-=a[i*m+k]*a[k*m+j];
+ a[i*m+j]=sum;
+ if((tmp=work[i]*FABS(sum))>=max){
+ max=tmp;
+ maxi=i;
+ }
+ }
+ if(j!=maxi){
+ for(k=0; k<m; ++k){
+ tmp=a[maxi*m+k];
+ a[maxi*m+k]=a[j*m+k];
+ a[j*m+k]=tmp;
+ }
+ work[maxi]=work[j];
+ }
+ idx[j]=maxi;
+ if(a[j*m+j]==0.0)
+ a[j*m+j]=LM_REAL_EPSILON;
+ if(j!=m-1){
+ tmp=LM_CNST(1.0)/(a[j*m+j]);
+ for(i=j+1; i<m; ++i)
+ a[i*m+j]*=tmp;
+ }
+ }
+
+ /* The decomposition has now replaced a. Solve the linear system using
+ * forward and back substitution
+ */
+ for(i=k=0; i<m; ++i){
+ j=idx[i];
+ sum=x[j];
+ x[j]=x[i];
+ if(k!=0)
+ for(j=k-1; j<i; ++j)
+ sum-=a[i*m+j]*x[j];
+ else
+ if(sum!=0.0)
+ k=i+1;
+ x[i]=sum;
+ }
+
+ for(i=m-1; i>=0; --i){
+ sum=x[i];
+ for(j=i+1; j<m; ++j)
+ sum-=a[i*m+j]*x[j];
+ x[i]=sum/a[i*m+i];
+ }
+
+#ifndef LINSOLVERS_RETAIN_MEMORY
+ free(buf);
+#endif
+
+ return 1;
+}
+
+/* undefine all. IT MUST REMAIN IN THIS POSITION IN FILE */
+#undef AX_EQ_B_LU
+
+#endif /* HAVE_LAPACK */
diff --git a/sci-libs/levmar/levmar-2.5/CMakeLists.txt b/sci-libs/levmar/levmar-2.5/CMakeLists.txt
new file mode 100644
index 000000000..0d02cb84e
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/CMakeLists.txt
@@ -0,0 +1,54 @@
+# levmar CMake file; see http://www.cmake.org and
+# http://www.insightsoftwareconsortium.org/wiki/index.php/CMake_Tutorial
+
+PROJECT(LEVMAR)
+#CMAKE_MINIMUM_REQUIRED(VERSION 1.4)
+
+# compiler flags
+#ADD_DEFINITIONS(-DLINSOLVERS_RETAIN_MEMORY) # do not free memory between linear solvers calls
+#REMOVE_DEFINITIONS(-DLINSOLVERS_RETAIN_MEMORY) # free memory between calls
+
+# f2c is sometimes equivalent to libF77 & libI77; in that case, set HAVE_F2C to 0
+SET(HAVE_F2C 1 CACHE BOOL "Do we have f2c or F77/I77?" )
+
+# the directory where the lapack/blas/f2c libraries reside
+SET(LAPACKBLAS_DIR /usr/lib CACHE PATH "Path to lapack/blas libraries")
+
+# actual names for the lapack/blas/f2c libraries
+SET(LAPACK_LIB lapack CACHE STRING "The name of the lapack library")
+SET(BLAS_LIB blas CACHE STRING "The name of the blas library")
+IF(HAVE_F2C)
+ SET(F2C_LIB f2c CACHE STRING "The name of the f2c library")
+ELSE(HAVE_F2C)
+ SET(F77_LIB libF77 CACHE STRING "The name of the F77 library")
+ SET(I77_LIB libI77 CACHE STRING "The name of the I77 library")
+ENDIF(HAVE_F2C)
+
+########################## NO CHANGES BEYOND THIS POINT ##########################
+
+INCLUDE_DIRECTORIES(.)
+#INCLUDE_DIRECTORIES(/usr/include)
+
+# levmar library source files
+ADD_LIBRARY(levmar STATIC
+ lm.c Axb.c misc.c lmlec.c lmbc.c lmblec.c lmbleic.c
+ levmar.h misc.h compiler.h
+)
+
+# demo program
+LINK_DIRECTORIES(${LAPACKBLAS_DIR})
+LINK_DIRECTORIES(.)
+ADD_EXECUTABLE(lmdemo lmdemo.c levmar.h)
+# libraries the demo depends on
+IF(HAVE_F2C)
+ TARGET_LINK_LIBRARIES(lmdemo levmar ${LAPACK_LIB} ${BLAS_LIB} ${F2C_LIB})
+ELSE(HAVE_F2C)
+ TARGET_LINK_LIBRARIES(lmdemo levmar ${LAPACK_LIB} ${BLAS_LIB} ${F77_LIB} ${I77_LIB})
+ENDIF(HAVE_F2C)
+
+# make sure that the library is built before the demo
+ADD_DEPENDENCIES(lmdemo levmar)
+
+#SUBDIRS(matlab)
+
+#ADD_TEST(levmar_tst lmdemo)
diff --git a/sci-libs/levmar/levmar-2.5/LICENSE b/sci-libs/levmar/levmar-2.5/LICENSE
new file mode 100644
index 000000000..d60c31a97
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/LICENSE
@@ -0,0 +1,340 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc.
+ 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
+ The licenses for most software are designed to take away your
+freedom to share and change it. By contrast, the GNU General Public
+License is intended to guarantee your freedom to share and change free
+software--to make sure the software is free for all its users. This
+General Public License applies to most of the Free Software
+Foundation's software and to any other program whose authors commit to
+using it. (Some other Free Software Foundation software is covered by
+the GNU Library General Public License instead.) You can apply it to
+your programs, too.
+
+ When we speak of free software, we are referring to freedom, not
+price. Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+this service if you wish), that you receive source code or can get it
+if you want it, that you can change the software or use pieces of it
+in new free programs; and that you know you can do these things.
+
+ To protect your rights, we need to make restrictions that forbid
+anyone to deny you these rights or to ask you to surrender the rights.
+These restrictions translate to certain responsibilities for you if you
+distribute copies of the software, or if you modify it.
+
+ For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must give the recipients all the rights that
+you have. You must make sure that they, too, receive or can get the
+source code. And you must show them these terms so they know their
+rights.
+
+ We protect your rights with two steps: (1) copyright the software, and
+(2) offer you this license which gives you legal permission to copy,
+distribute and/or modify the software.
+
+ Also, for each author's protection and ours, we want to make certain
+that everyone understands that there is no warranty for this free
+software. If the software is modified by someone else and passed on, we
+want its recipients to know that what they have is not the original, so
+that any problems introduced by others will not reflect on the original
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+
+ Finally, any free program is threatened constantly by software
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+
+ The precise terms and conditions for copying, distribution and
+modification follow.
+
+ GNU GENERAL PUBLIC LICENSE
+ TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+ 0. This License applies to any program or other work which contains
+a notice placed by the copyright holder saying it may be distributed
+under the terms of this General Public License. The "Program", below,
+refers to any such program or work, and a "work based on the Program"
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+that is to say, a work containing the Program or a portion of it,
+either verbatim or with modifications and/or translated into another
+language. (Hereinafter, translation is included without limitation in
+the term "modification".) Each licensee is addressed as "you".
+
+Activities other than copying, distribution and modification are not
+covered by this License; they are outside its scope. The act of
+running the Program is not restricted, and the output from the Program
+is covered only if its contents constitute a work based on the
+Program (independent of having been made by running the Program).
+Whether that is true depends on what the Program does.
+
+ 1. You may copy and distribute verbatim copies of the Program's
+source code as you receive it, in any medium, provided that you
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+notices that refer to this License and to the absence of any warranty;
+and give any other recipients of the Program a copy of this License
+along with the Program.
+
+You may charge a fee for the physical act of transferring a copy, and
+you may at your option offer warranty protection in exchange for a fee.
+
+ 2. You may modify your copy or copies of the Program or any portion
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+
+ a) You must cause the modified files to carry prominent notices
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+
+ b) You must cause any work that you distribute or publish, that in
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+
+ c) If the modified program normally reads commands interactively
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+ interactive use in the most ordinary way, to print or display an
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+ a warranty) and that users may redistribute the program under
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+
+These requirements apply to the modified work as a whole. If
+identifiable sections of that work are not derived from the Program,
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+Thus, it is not the intent of this section to claim rights or contest
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+ it under the terms of the GNU General Public License as published by
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+ (at your option) any later version.
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+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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+ Gnomovision version 69, Copyright (C) year name of author
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+ This is free software, and you are welcome to redistribute it
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+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, the commands you use may
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+
+ <signature of Ty Coon>, 1 April 1989
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+
+This General Public License does not permit incorporating your program into
+proprietary programs. If your program is a subroutine library, you may
+consider it more useful to permit linking proprietary applications with the
+library. If this is what you want to do, use the GNU Library General
+Public License instead of this License.
diff --git a/sci-libs/levmar/levmar-2.5/Makefile b/sci-libs/levmar/levmar-2.5/Makefile
new file mode 100644
index 000000000..14525e223
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/Makefile
@@ -0,0 +1,47 @@
+#
+# Unix/Linux GCC Makefile for Levenberg - Marquardt minimization
+# Under windows, use Makefile.vc for MSVC
+#
+
+CONFIGFLAGS=#-ULINSOLVERS_RETAIN_MEMORY
+#ARCHFLAGS=-march=pentium4 # YOU MIGHT WANT TO UNCOMMENT THIS FOR P4
+LIBOBJS=lm.o Axb.o misc.o lmlec.o lmbc.o lmblec.o lmbleic.o
+LIBSRCS=lm.c Axb.c misc.c lmlec.c lmbc.c lmblec.c lmbleic.c
+DEMOBJS=lmdemo.o
+DEMOSRCS=lmdemo.c
+AR=ar
+RANLIB=ranlib
+
+LIBS=$(LAPACKLIBS)
+
+all: liblevmar.a lmdemo
+
+liblevmar.a: $(LIBOBJS)
+ $(AR) crv liblevmar.a $(LIBOBJS)
+ $(RANLIB) liblevmar.a
+
+lmdemo: $(DEMOBJS) liblevmar.a
+ $(CC) $(LDFLAGS) $(DEMOBJS) -o lmdemo liblevmar.a $(LIBS) -lm
+
+lm.o: lm.c lm_core.c levmar.h misc.h compiler.h
+Axb.o: Axb.c Axb_core.c levmar.h misc.h
+misc.o: misc.c misc_core.c levmar.h misc.h
+lmlec.o: lmlec.c lmlec_core.c levmar.h misc.h
+lmbc.o: lmbc.c lmbc_core.c levmar.h misc.h compiler.h
+lmblec.o: lmblec.c lmblec_core.c levmar.h misc.h
+lmbleic.o: lmbleic.c lmbleic_core.c levmar.h misc.h
+
+lmdemo.o: levmar.h
+
+clean:
+ @rm -f $(LIBOBJS) $(DEMOBJS)
+
+cleanall: clean
+ @rm -f lmdemo
+ @rm -f liblevmar.a
+
+depend:
+ makedepend -f Makefile $(LIBSRCS) $(DEMOSRCS)
+
+# DO NOT DELETE THIS LINE -- make depend depends on it.
+
diff --git a/sci-libs/levmar/levmar-2.5/Makefile.icc b/sci-libs/levmar/levmar-2.5/Makefile.icc
new file mode 100644
index 000000000..a2ab2e72e
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/Makefile.icc
@@ -0,0 +1,62 @@
+#
+# Unix/Linux Intel ICC Makefile for Levenberg - Marquardt minimization
+# To be used with "make -f Makefile.icc"
+# Under windows, use Makefile.vc for MSVC
+#
+
+CC=icc #-w1 # warnings on
+CXX=icpc
+CONFIGFLAGS=#-ULINSOLVERS_RETAIN_MEMORY
+ARCHFLAGS=-march=pentium4 -mcpu=pentium4
+CFLAGS=$(CONFIGFLAGS) $(ARCHFLAGS) -O3 -tpp7 -xW -ip -ipo -unroll #-g
+LAPACKLIBS_PATH=/usr/local/lib # WHEN USING LAPACK, CHANGE THIS TO WHERE YOUR COMPILED LIBS ARE!
+LDFLAGS=-L$(LAPACKLIBS_PATH) -L.
+LIBOBJS=lm.o Axb.o misc.o lmlec.o lmbc.o lmblec.o lmbleic.o
+LIBSRCS=lm.c Axb.c misc.c lmlec.c lmbc.c lmblec.c lmbleic.c
+DEMOBJS=lmdemo.o
+DEMOSRCS=lmdemo.c
+AR=xiar
+#RANLIB=ranlib
+LAPACKLIBS=-llapack -lblas -lf2c # comment this line if you are not using LAPACK.
+ # On systems with a FORTRAN (not f2c'ed) version of LAPACK, -lf2c is
+ # not necessary; on others, -lf2c is equivalent to -lF77 -lI77
+
+# The following works with the ATLAS updated lapack and Linux_P4SSE2 from http://www.netlib.org/atlas/archives/linux/
+#LAPACKLIBS=-L/usr/local/atlas/lib -llapack -lcblas -lf77blas -latlas -lf2c
+
+#LAPACKLIBS=-llapack -lgoto2 -lpthread -lf2c # This works with GotoBLAS
+ # from http://www.tacc.utexas.edu/research-development/tacc-projects/
+
+LIBS=$(LAPACKLIBS)
+
+all: liblevmar.a lmdemo
+
+liblevmar.a: $(LIBOBJS)
+ $(AR) crv liblevmar.a $(LIBOBJS)
+ #$(RANLIB) liblevmar.a
+
+lmdemo: $(DEMOBJS) liblevmar.a
+ $(CC) $(ARCHFLAGS) $(LDFLAGS) $(DEMOBJS) -o lmdemo -llevmar $(LIBS) -lm
+
+lm.o: lm.c lm_core.c levmar.h misc.h compiler.h
+Axb.o: Axb.c Axb_core.c levmar.h misc.h
+misc.o: misc.c misc_core.c levmar.h misc.h
+lmlec.o: lmlec.c lmlec_core.c levmar.h misc.h
+lmbc.o: lmbc.c lmbc_core.c levmar.h misc.h compiler.h
+lmblec.o: lmblec.c lmblec_core.c levmar.h misc.h
+lmbleic.o: lmbleic.c lmbleic_core.c levmar.h misc.h
+
+lmdemo.o: levmar.h
+
+clean:
+ @rm -f $(LIBOBJS) $(DEMOBJS)
+
+cleanall: clean
+ @rm -f lmdemo
+ @rm -f liblevmar.a
+
+depend:
+ makedepend -f Makefile.icc $(LIBSRCS) $(DEMOSRCS)
+
+# DO NOT DELETE THIS LINE -- make depend depends on it.
+
diff --git a/sci-libs/levmar/levmar-2.5/Makefile.so b/sci-libs/levmar/levmar-2.5/Makefile.so
new file mode 100644
index 000000000..fac088c7c
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/Makefile.so
@@ -0,0 +1,49 @@
+#
+# Unix/Linux GCC Makefile for Levenberg - Marquardt minimization
+# Under windows, use Makefile.vc for MSVC
+#
+
+# major & minor shared lib numbers
+MAJ=2
+MIN=2
+ODIR=sobj # where to place object files for shared lib
+CC=gcc
+CONFIGFLAGS=-ULINSOLVERS_RETAIN_MEMORY
+#ARCHFLAGS=-march=pentium4 # YOU MIGHT WANT TO UNCOMMENT THIS FOR P4
+CFLAGS=-fPIC $(CONFIGFLAGS) $(ARCHFLAGS) -O3 -funroll-loops -Wall #-pg
+LAPACKLIBS_PATH=/usr/local/lib # WHEN USING LAPACK, CHANGE THIS TO WHERE YOUR COMPILED LIBS ARE!
+LIBOBJS=$(ODIR)/lm.o $(ODIR)/Axb.o $(ODIR)/misc.o $(ODIR)/lmlec.o $(ODIR)/lmbc.o $(ODIR)/lmblec.o $(ODIR)/lmbleic.o
+LIBSRCS=lm.c Axb.c misc.c lmlec.c lmbc.c lmblec.c lmbleic.c
+LAPACKLIBS=-llapack -lblas -lf2c # comment this line if you are not using LAPACK.
+ # On systems with a FORTRAN (not f2c'ed) version of LAPACK, -lf2c is
+ # not necessary; on others, -lf2c is equivalent to -lF77 -lI77
+
+LIBS=$(LAPACKLIBS)
+
+$(ODIR)/liblevmar.so.$(MAJ).$(MIN): $(LIBOBJS)
+ $(CC) -shared -Wl,-soname,liblevmar.so.$(MAJ) -o $(ODIR)/liblevmar.so.$(MAJ).$(MIN) $(LIBOBJS) #-llapack -lblas -lf2c
+
+# implicit rule for generating *.o files in ODIR from *.c files
+$(ODIR)/%.o : %.c
+ $(CC) $(CFLAGS) -c $< -o $@
+
+
+$(ODIR)/lm.o: lm.c lm_core.c levmar.h misc.h compiler.h
+$(ODIR)/Axb.o: Axb.c Axb_core.c levmar.h misc.h
+$(ODIR)/misc.o: misc.c misc_core.c levmar.h misc.h
+$(ODIR)/lmlec.o: lmlec.c lmlec_core.c levmar.h misc.h
+$(ODIR)/lmbc.o: lmbc.c lmbc_core.c levmar.h misc.h compiler.h
+$(ODIR)/lmblec.o: lmblec.c lmblec_core.c levmar.h misc.h
+$(ODIR)/lmbleic.o: lmbleic.c lmbleic_core.c levmar.h misc.h
+
+clean:
+ @rm -f $(LIBOBJS)
+
+cleanall: clean
+ @rm -f $(ODIR)/liblevmar.so.$(MAJ).$(MIN)
+
+depend:
+ makedepend -f Makefile $(LIBSRCS)
+
+# DO NOT DELETE THIS LINE -- make depend depends on it.
+
diff --git a/sci-libs/levmar/levmar-2.5/Makefile.vc b/sci-libs/levmar/levmar-2.5/Makefile.vc
new file mode 100644
index 000000000..b8ae99aa2
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/Makefile.vc
@@ -0,0 +1,59 @@
+#
+# MS Visual C Makefile for Levenberg - Marquardt minimization
+# Under Unix/Linux, use Makefile for GCC
+#
+# At the command prompt, type
+# nmake /f Makefile.vc
+#
+# NOTE: To use this, you must have MSVC installed and properly
+# configured for command line use (you might need to run VCVARS32.BAT
+# included with your copy of MSVC). Another option is to use the
+# free MSVC toolkit from http://msdn.microsoft.com/visualc/vctoolkit2003/
+#
+
+MAKE=nmake /nologo
+CC=cl /nologo
+CONFIGFLAGS=#/ULINSOLVERS_RETAIN_MEMORY
+# YOU MIGHT WANT TO UNCOMMENT THE FOLLOWING LINE
+#SPOPTFLAGS=/GL /G7 /arch:SSE2 # special optimization: resp. whole program opt., Athlon/Pentium4 opt., SSE2 extensions
+# /MD COMPILES WITH MULTIPLE THREADS SUPPORT. TO DISABLE IT, SUBSTITUTE WITH /ML
+# FLAG /EHsc SUPERSEDED /GX IN MSVC'05. IF YOU HAVE AN EARLIER VERSION THAT COMPLAINS ABOUT IT, CHANGE /EHsc TO /GX
+CFLAGS=$(CONFIGFLAGS) /I. /MD /W3 /EHsc /O2 $(SPOPTFLAGS) # /Wall
+LAPACKLIBS_PATH=C:\src\lib # WHEN USING LAPACK, CHANGE THIS TO WHERE YOUR COMPILED LIBS ARE!
+LDFLAGS=/link /subsystem:console /opt:ref /libpath:$(LAPACKLIBS_PATH) /libpath:.
+LIBOBJS=lm.obj Axb.obj misc.obj lmlec.obj lmbc.obj lmblec.obj lmbleic.obj
+LIBSRCS=lm.c Axb.c misc.c lmlec.c lmbc.c lmblec.c lmbleic.c
+DEMOBJS=lmdemo.obj
+DEMOSRCS=lmdemo.c
+AR=lib /nologo
+
+# comment the following line if you are not using LAPACK
+LAPACKLIBS=clapack.lib blas.lib libF77.lib libI77.lib
+
+LIBS=levmar.lib $(LAPACKLIBS)
+
+all: levmar.lib lmdemo.exe
+
+levmar.lib: $(LIBOBJS)
+ $(AR) /out:levmar.lib $(LIBOBJS)
+
+lmdemo.exe: $(DEMOBJS) levmar.lib
+ $(CC) $(DEMOBJS) $(LDFLAGS) /out:lmdemo.exe $(LIBS)
+
+lm.obj: lm.c lm_core.c levmar.h misc.h compiler.h
+Axb.obj: Axb.c Axb_core.c levmar.h misc.h
+misc.obj: misc.c misc_core.c levmar.h misc.h
+lmlec.obj: lmlec.c lmlec_core.c levmar.h misc.h
+lmbc.obj: lmbc.c lmbc_core.c levmar.h misc.h compiler.h
+lmblec.obj: lmblec.c lmblec_core.c levmar.h misc.h
+lmbleic.obj: lmbleic.c lmbleic_core.c levmar.h misc.h
+
+lmdemo.obj: levmar.h
+
+clean:
+ -del $(LIBOBJS) $(DEMOBJS)
+
+cleanall: clean
+ -del lmdemo.exe
+ -del levmar.lib
+
diff --git a/sci-libs/levmar/levmar-2.5/README.txt b/sci-libs/levmar/levmar-2.5/README.txt
new file mode 100644
index 000000000..44da92998
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/README.txt
@@ -0,0 +1,74 @@
+ **************************************************************
+ LEVMAR
+ version 2.5
+ By Manolis Lourakis
+
+ Institute of Computer Science
+ Foundation for Research and Technology - Hellas
+ Heraklion, Crete, Greece
+ **************************************************************
+
+
+GENERAL
+This is levmar, a copylefted C/C++ implementation of the Levenberg-Marquardt non-linear
+least squares algorithm. levmar includes double and single precision LM versions, both
+with analytic and finite difference approximated Jacobians. levmar also has some support
+for constrained non-linear least squares, allowing linear equation, box and linear
+inequality constraints. The following options regarding the solution of the underlying
+augmented normal equations are offered:
+
+1) Assuming that you have LAPACK (or an equivalent vendor library such as ESSL, MKL,
+ NAG, ...) installed, you can use the included LAPACK-based solvers (default).
+
+2) If you don't have LAPACK or decide not to use it, undefine HAVE_LAPACK in levmar.h
+ and a LAPACK-free, LU-based linear systems solver will by used. Also, the line
+ setting the variable LAPACKLIBS in the Makefile should be commented out.
+
+It is strongly recommended that you *do* employ LAPACK; if you don't have it already,
+I suggest getting clapack from http://www.netlib.org/clapack. However, LAPACK's
+use is not mandatory and the 2nd option makes levmar totally self-contained.
+See lmdemo.c for examples of use and http://www.ics.forth.gr/~lourakis/levmar
+for general comments. An example of using levmar for data fitting is in expfit.c
+
+The mathematical theory behind levmar is described in the lecture notes entitled
+"Methods for Non-Linear Least Squares Problems", by K. Madsen, H.B. Nielsen and O. Tingleff,
+Technical University of Denmark (http://www.imm.dtu.dk/courses/02611/nllsq.pdf).
+
+LICENSE
+levmar is released under the GNU Public License (GPL), which can be found in the included
+LICENSE file. Note that under the terms of GPL, commercial use is allowed only if a software
+employing levmar is also published in source under the GPL. However, if you are interested
+in using levmar in a proprietary commercial application, a commercial license for levmar
+can be obtained by contacting the author using the email address at the end of this file.
+
+COMPILATION
+ - You might first consider setting a few configuration options at the top of
+ levmar.h. See the accompanying comments for more details.
+
+ - On a Linux/Unix system, typing "make" will build both levmar and the demo
+ program using gcc. Alternatively, if Intel's C++ compiler is installed, it
+ can be used by typing "make -f Makefile.icc".
+
+ - Under Windows and if Visual C is installed & configured for command line
+ use, type "nmake /f Makefile.vc" in a cmd window to build levmar and the
+ demo program. In case of trouble, read the comments on top of Makefile.vc
+ Visual C++ project files (levmar.vcproj and lmdemo.vcproj) are also included,
+ however they are not supported and are only meant to serve as a starting point
+ for creating your own. Check http://www.arstdesign.com/articles/prjconverter.html
+ if you need to convert to .dsw/.dsp (i.e., Visual C++ 6.0) project files.
+
+ - levmar can also be built under various platforms using the CMake cross-platform
+ build system. The included CMakeLists.txt file can be used to generate makefiles
+ for Unix systems or project files for Windows systems. See http://www.cmake.org
+ for details.
+
+MATLAB INTERFACE
+Since version 2.2, the levmar distribution includes a matlab interface.
+See the 'matlab' subdirectory for more information and examples of use.
+
+Notice that *_core.c files are not to be compiled directly; For example,
+Axb_core.c is included by Axb.c, to provide single and double precision
+routine versions.
+
+
+Send your comments/bug reports to lourakis at ics forth gr
diff --git a/sci-libs/levmar/levmar-2.5/compiler.h b/sci-libs/levmar/levmar-2.5/compiler.h
new file mode 100644
index 000000000..706924c6a
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/compiler.h
@@ -0,0 +1,45 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+#ifndef _COMPILER_H_
+#define _COMPILER_H_
+
+/* note: intel's icc defines both __ICC & __INTEL_COMPILER.
+ * Also, some compilers other than gcc define __GNUC__,
+ * therefore gcc should be checked last
+ */
+#ifdef _MSC_VER
+#define inline __inline // MSVC
+#elif !defined(__ICC) && !defined(__INTEL_COMPILER) && !defined(__GNUC__)
+#define inline // other than MSVC, ICC, GCC: define empty
+#endif
+
+#ifdef _MSC_VER
+#define LM_FINITE _finite // MSVC
+#elif defined(__ICC) || defined(__INTEL_COMPILER) || defined(__GNUC__)
+#define LM_FINITE finite // ICC, GCC
+#else
+#define LM_FINITE finite // other than MSVC, ICC, GCC, let's hope this will work
+#endif
+
+#ifdef _MSC_VER // avoid deprecation warnings in VS2005
+#define _CRT_SECURE_NO_WARNINGS
+#endif
+
+#endif /* _COMPILER_H_ */
diff --git a/sci-libs/levmar/levmar-2.5/expfit.c b/sci-libs/levmar/levmar-2.5/expfit.c
new file mode 100644
index 000000000..21d54f4e7
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/expfit.c
@@ -0,0 +1,122 @@
+////////////////////////////////////////////////////////////////////////////////////
+// Example program that shows how to use levmar in order to fit the three-
+// parameter exponential model x_i = p[0]*exp(-p[1]*i) + p[2] to a set of
+// data measurements; example is based on a similar one from GSL.
+//
+// Copyright (C) 2008 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+////////////////////////////////////////////////////////////////////////////////////
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include <levmar.h>
+
+#ifndef LM_DBL_PREC
+#error Example program assumes that levmar has been compiled with double precision, see LM_DBL_PREC!
+#endif
+
+
+/* the following macros concern the initialization of a random number generator for adding noise */
+#undef REPEATABLE_RANDOM
+#define DBL_RAND_MAX (double)(RAND_MAX)
+
+#ifdef _MSC_VER // MSVC
+#include <process.h>
+#define GETPID _getpid
+#elif defined(__GNUC__) // GCC
+#include <sys/types.h>
+#include <unistd.h>
+#define GETPID getpid
+#else
+#warning Do not know the name of the function returning the process id for your OS/compiler combination
+#define GETPID 0
+#endif /* _MSC_VER */
+
+#ifdef REPEATABLE_RANDOM
+#define INIT_RANDOM(seed) srandom(seed)
+#else
+#define INIT_RANDOM(seed) srandom((int)GETPID()) // seed unused
+#endif
+
+/* Gaussian noise with mean m and variance s, uses the Box-Muller transformation */
+double gNoise(double m, double s)
+{
+double r1, r2, val;
+
+ r1=((double)random())/DBL_RAND_MAX;
+ r2=((double)random())/DBL_RAND_MAX;
+
+ val=sqrt(-2.0*log(r1))*cos(2.0*M_PI*r2);
+
+ val=s*val+m;
+
+ return val;
+}
+
+/* model to be fitted to measurements: x_i = p[0]*exp(-p[1]*i) + p[2], i=0...n-1 */
+void expfunc(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ for(i=0; i<n; ++i){
+ x[i]=p[0]*exp(-p[1]*i) + p[2];
+ }
+}
+
+/* Jacobian of expfunc() */
+void jacexpfunc(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+
+ /* fill Jacobian row by row */
+ for(i=j=0; i<n; ++i){
+ jac[j++]=exp(-p[1]*i);
+ jac[j++]=-p[0]*i*exp(-p[1]*i);
+ jac[j++]=1.0;
+ }
+}
+
+int main()
+{
+const int n=40, m=3; // 40 measurements, 3 parameters
+double p[m], x[n], opts[LM_OPTS_SZ], info[LM_INFO_SZ];
+register int i;
+int ret;
+
+ /* generate some measurement using the exponential model with
+ * parameters (5.0, 0.1, 1.0), corrupted with zero-mean
+ * Gaussian noise of s=0.1
+ */
+ INIT_RANDOM(0);
+ for(i=0; i<n; ++i)
+ x[i]=(5.0*exp(-0.1*i) + 1.0) + gNoise(0.0, 0.1);
+
+ /* initial parameters estimate: (1.0, 0.0, 0.0) */
+ p[0]=1.0; p[1]=0.0; p[2]=0.0;
+
+ /* optimization control parameters; passing to levmar NULL instead of opts reverts to defaults */
+ opts[0]=LM_INIT_MU; opts[1]=1E-15; opts[2]=1E-15; opts[3]=1E-20;
+ opts[4]=LM_DIFF_DELTA; // relevant only if the finite difference Jacobian version is used
+
+ /* invoke the optimization function */
+ ret=dlevmar_der(expfunc, jacexpfunc, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ //ret=dlevmar_dif(expfunc, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // without Jacobian
+ printf("Levenberg-Marquardt returned in %g iter, reason %g, sumsq %g [%g]\n", info[5], info[6], info[1], info[0]);
+ printf("Best fit parameters: %.7g %.7g %.7g\n", p[0], p[1], p[2]);
+
+ exit(0);
+}
diff --git a/sci-libs/levmar/levmar-2.5/levmar.h b/sci-libs/levmar/levmar-2.5/levmar.h
new file mode 100644
index 000000000..96b5e81cd
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/levmar.h
@@ -0,0 +1,370 @@
+/*
+////////////////////////////////////////////////////////////////////////////////////
+//
+// Prototypes and definitions for the Levenberg - Marquardt minimization algorithm
+// Copyright (C) 2004 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+////////////////////////////////////////////////////////////////////////////////////
+*/
+
+#ifndef _LEVMAR_H_
+#define _LEVMAR_H_
+
+
+/************************************* Start of configuration options *************************************/
+
+/* specify whether to use LAPACK or not. The first option is strongly recommended */
+#define HAVE_LAPACK /* use LAPACK */
+/* #undef HAVE_LAPACK */ /* uncomment this to force not using LAPACK */
+
+/* to avoid the overhead of repeated mallocs(), routines in Axb.c can be instructed to
+ * retain working memory between calls. Such a choice, however, renders these routines
+ * non-reentrant and is not safe in a shared memory multiprocessing environment.
+ * Bellow, this option is turned on only when not compiling with OpenMP.
+ */
+#if !defined(_OPENMP)
+#define LINSOLVERS_RETAIN_MEMORY /* comment this if you don't want routines in Axb.c retain working memory between calls */
+#endif
+
+/* determine the precision variants to be build. Default settings build
+ * both the single and double precision routines
+ */
+#define LM_DBL_PREC /* comment this if you don't want the double precision routines to be compiled */
+#define LM_SNGL_PREC /* comment this if you don't want the single precision routines to be compiled */
+
+/****************** End of configuration options, no changes necessary beyond this point ******************/
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+
+#define FABS(x) (((x)>=0.0)? (x) : -(x))
+
+/* work arrays size for ?levmar_der and ?levmar_dif functions.
+ * should be multiplied by sizeof(double) or sizeof(float) to be converted to bytes
+ */
+#define LM_DER_WORKSZ(npar, nmeas) (2*(nmeas) + 4*(npar) + (nmeas)*(npar) + (npar)*(npar))
+#define LM_DIF_WORKSZ(npar, nmeas) (4*(nmeas) + 4*(npar) + (nmeas)*(npar) + (npar)*(npar))
+
+/* work arrays size for ?levmar_bc_der and ?levmar_bc_dif functions.
+ * should be multiplied by sizeof(double) or sizeof(float) to be converted to bytes
+ */
+#define LM_BC_DER_WORKSZ(npar, nmeas) (2*(nmeas) + 4*(npar) + (nmeas)*(npar) + (npar)*(npar))
+#define LM_BC_DIF_WORKSZ(npar, nmeas) LM_BC_DER_WORKSZ((npar), (nmeas)) /* LEVMAR_BC_DIF currently implemented using LEVMAR_BC_DER()! */
+
+/* work arrays size for ?levmar_lec_der and ?levmar_lec_dif functions.
+ * should be multiplied by sizeof(double) or sizeof(float) to be converted to bytes
+ */
+#define LM_LEC_DER_WORKSZ(npar, nmeas, nconstr) LM_DER_WORKSZ((npar)-(nconstr), (nmeas))
+#define LM_LEC_DIF_WORKSZ(npar, nmeas, nconstr) LM_DIF_WORKSZ((npar)-(nconstr), (nmeas))
+
+/* work arrays size for ?levmar_blec_der and ?levmar_blec_dif functions.
+ * should be multiplied by sizeof(double) or sizeof(float) to be converted to bytes
+ */
+#define LM_BLEC_DER_WORKSZ(npar, nmeas, nconstr) LM_LEC_DER_WORKSZ((npar), (nmeas)+(npar), (nconstr))
+#define LM_BLEC_DIF_WORKSZ(npar, nmeas, nconstr) LM_LEC_DIF_WORKSZ((npar), (nmeas)+(npar), (nconstr))
+
+/* work arrays size for ?levmar_bleic_der and ?levmar_bleic_dif functions.
+ * should be multiplied by sizeof(double) or sizeof(float) to be converted to bytes
+ */
+#define LM_BLEIC_DER_WORKSZ(npar, nmeas, nconstr1, nconstr2) LM_BLEC_DER_WORKSZ((npar)+(nconstr2), (nmeas)+(nconstr2), (nconstr1)+(nconstr2))
+#define LM_BLEIC_DIF_WORKSZ(npar, nmeas, nconstr1, nconstr2) LM_BLEC_DIF_WORKSZ((npar)+(nconstr2), (nmeas)+(nconstr2), (nconstr1)+(nconstr2))
+
+#define LM_OPTS_SZ 5 /* max(4, 5) */
+#define LM_INFO_SZ 10
+#define LM_ERROR -1
+#define LM_INIT_MU 1E-03
+#define LM_STOP_THRESH 1E-17
+#define LM_DIFF_DELTA 1E-06
+#define LM_VERSION "2.5 (December 2009)"
+
+#ifdef LM_DBL_PREC
+/* double precision LM, with & without Jacobian */
+/* unconstrained minimization */
+extern int dlevmar_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, int itmax, double *opts,
+ double *info, double *work, double *covar, void *adata);
+
+extern int dlevmar_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, int itmax, double *opts,
+ double *info, double *work, double *covar, void *adata);
+
+/* box-constrained minimization */
+extern int dlevmar_bc_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+extern int dlevmar_bc_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+#ifdef HAVE_LAPACK
+/* linear equation constrained minimization */
+extern int dlevmar_lec_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *A, double *b, int k,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+extern int dlevmar_lec_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *A, double *b, int k,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+/* box & linear equation constrained minimization */
+extern int dlevmar_blec_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub, double *A, double *b, int k, double *wghts,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+extern int dlevmar_blec_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub, double *A, double *b, int k, double *wghts,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+/* box, linear equations & inequalities constrained minimization */
+extern int dlevmar_bleic_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub,
+ double *A, double *b, int k1, double *C, double *d, int k2,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+extern int dlevmar_bleic_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub,
+ double *A, double *b, int k1, double *C, double *d, int k2,
+ int itmax, double *opts, double *info, double *work, double *covar, void *adata);
+
+/* box & linear inequality constraints */
+extern int dlevmar_blic_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub, double *C, double *d, int k2,
+ int itmax, double opts[4], double info[LM_INFO_SZ], double *work, double *covar, void *adata);
+
+extern int dlevmar_blic_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *lb, double *ub, double *C, double *d, int k2,
+ int itmax, double opts[5], double info[LM_INFO_SZ], double *work, double *covar, void *adata);
+
+/* linear equation & inequality constraints */
+extern int dlevmar_leic_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *A, double *b, int k1, double *C, double *d, int k2,
+ int itmax, double opts[4], double info[LM_INFO_SZ], double *work, double *covar, void *adata);
+
+extern int dlevmar_leic_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *A, double *b, int k1, double *C, double *d, int k2,
+ int itmax, double opts[5], double info[LM_INFO_SZ], double *work, double *covar, void *adata);
+
+/* linear inequality constraints */
+extern int dlevmar_lic_der(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *C, double *d, int k2,
+ int itmax, double opts[4], double info[LM_INFO_SZ], double *work, double *covar, void *adata);
+
+extern int dlevmar_lic_dif(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *x, int m, int n, double *C, double *d, int k2,
+ int itmax, double opts[5], double info[LM_INFO_SZ], double *work, double *covar, void *adata);
+#endif /* HAVE_LAPACK */
+
+#endif /* LM_DBL_PREC */
+
+
+#ifdef LM_SNGL_PREC
+/* single precision LM, with & without Jacobian */
+/* unconstrained minimization */
+extern int slevmar_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, int itmax, float *opts,
+ float *info, float *work, float *covar, void *adata);
+
+extern int slevmar_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, int itmax, float *opts,
+ float *info, float *work, float *covar, void *adata);
+
+/* box-constrained minimization */
+extern int slevmar_bc_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+extern int slevmar_bc_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+#ifdef HAVE_LAPACK
+/* linear equation constrained minimization */
+extern int slevmar_lec_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *A, float *b, int k,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+extern int slevmar_lec_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *A, float *b, int k,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+/* box & linear equation constrained minimization */
+extern int slevmar_blec_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub, float *A, float *b, int k, float *wghts,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+extern int slevmar_blec_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub, float *A, float *b, int k, float *wghts,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+/* box, linear equations & inequalities constrained minimization */
+extern int slevmar_bleic_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub,
+ float *A, float *b, int k1, float *C, float *d, int k2,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+extern int slevmar_bleic_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub,
+ float *A, float *b, int k1, float *C, float *d, int k2,
+ int itmax, float *opts, float *info, float *work, float *covar, void *adata);
+
+/* box & linear inequality constraints */
+extern int slevmar_blic_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub, float *C, float *d, int k2,
+ int itmax, float opts[4], float info[LM_INFO_SZ], float *work, float *covar, void *adata);
+
+extern int slevmar_blic_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *lb, float *ub, float *C, float *d, int k2,
+ int itmax, float opts[5], float info[LM_INFO_SZ], float *work, float *covar, void *adata);
+
+/* linear equality & inequality constraints */
+extern int slevmar_leic_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *A, float *b, int k1, float *C, float *d, int k2,
+ int itmax, float opts[4], float info[LM_INFO_SZ], float *work, float *covar, void *adata);
+
+extern int slevmar_leic_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *A, float *b, int k1, float *C, float *d, int k2,
+ int itmax, float opts[5], float info[LM_INFO_SZ], float *work, float *covar, void *adata);
+
+/* linear inequality constraints */
+extern int slevmar_lic_der(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *C, float *d, int k2,
+ int itmax, float opts[4], float info[LM_INFO_SZ], float *work, float *covar, void *adata);
+
+extern int slevmar_lic_dif(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *x, int m, int n, float *C, float *d, int k2,
+ int itmax, float opts[5], float info[LM_INFO_SZ], float *work, float *covar, void *adata);
+#endif /* HAVE_LAPACK */
+
+#endif /* LM_SNGL_PREC */
+
+/* linear system solvers */
+#ifdef HAVE_LAPACK
+
+#ifdef LM_DBL_PREC
+extern int dAx_eq_b_QR(double *A, double *B, double *x, int m);
+extern int dAx_eq_b_QRLS(double *A, double *B, double *x, int m, int n);
+extern int dAx_eq_b_Chol(double *A, double *B, double *x, int m);
+extern int dAx_eq_b_LU(double *A, double *B, double *x, int m);
+extern int dAx_eq_b_SVD(double *A, double *B, double *x, int m);
+extern int dAx_eq_b_BK(double *A, double *B, double *x, int m);
+#endif /* LM_DBL_PREC */
+
+#ifdef LM_SNGL_PREC
+extern int sAx_eq_b_QR(float *A, float *B, float *x, int m);
+extern int sAx_eq_b_QRLS(float *A, float *B, float *x, int m, int n);
+extern int sAx_eq_b_Chol(float *A, float *B, float *x, int m);
+extern int sAx_eq_b_LU(float *A, float *B, float *x, int m);
+extern int sAx_eq_b_SVD(float *A, float *B, float *x, int m);
+extern int sAx_eq_b_BK(float *A, float *B, float *x, int m);
+#endif /* LM_SNGL_PREC */
+
+#else /* no LAPACK */
+
+#ifdef LM_DBL_PREC
+extern int dAx_eq_b_LU_noLapack(double *A, double *B, double *x, int n);
+#endif /* LM_DBL_PREC */
+
+#ifdef LM_SNGL_PREC
+extern int sAx_eq_b_LU_noLapack(float *A, float *B, float *x, int n);
+#endif /* LM_SNGL_PREC */
+
+#endif /* HAVE_LAPACK */
+
+/* Jacobian verification, double & single precision */
+#ifdef LM_DBL_PREC
+extern void dlevmar_chkjac(
+ void (*func)(double *p, double *hx, int m, int n, void *adata),
+ void (*jacf)(double *p, double *j, int m, int n, void *adata),
+ double *p, int m, int n, void *adata, double *err);
+#endif /* LM_DBL_PREC */
+
+#ifdef LM_SNGL_PREC
+extern void slevmar_chkjac(
+ void (*func)(float *p, float *hx, int m, int n, void *adata),
+ void (*jacf)(float *p, float *j, int m, int n, void *adata),
+ float *p, int m, int n, void *adata, float *err);
+#endif /* LM_SNGL_PREC */
+
+/* standard deviation, coefficient of determination (R2) & Pearson's correlation coefficient for best-fit parameters */
+#ifdef LM_DBL_PREC
+extern double dlevmar_stddev( double *covar, int m, int i);
+extern double dlevmar_corcoef(double *covar, int m, int i, int j);
+extern double dlevmar_R2(void (*func)(double *p, double *hx, int m, int n, void *adata), double *p, double *x, int m, int n, void *adata);
+
+#endif /* LM_DBL_PREC */
+
+#ifdef LM_SNGL_PREC
+extern float slevmar_stddev( float *covar, int m, int i);
+extern float slevmar_corcoef(float *covar, int m, int i, int j);
+extern float slevmar_R2(void (*func)(float *p, float *hx, int m, int n, void *adata), float *p, float *x, int m, int n, void *adata);
+#endif /* LM_SNGL_PREC */
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _LEVMAR_H_ */
diff --git a/sci-libs/levmar/levmar-2.5/levmar.vcproj b/sci-libs/levmar/levmar-2.5/levmar.vcproj
new file mode 100644
index 000000000..214db23c5
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/levmar.vcproj
@@ -0,0 +1,200 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<VisualStudioProject
+ ProjectType="Visual C++"
+ Version="8,00"
+ Name="levmar"
+ ProjectGUID="{F329E490-DB04-453A-A0BF-FEB90BD949D8}"
+ Keyword="Win32Proj"
+ >
+ <Platforms>
+ <Platform
+ Name="Win32"
+ />
+ </Platforms>
+ <ToolFiles>
+ </ToolFiles>
+ <Configurations>
+ <Configuration
+ Name="Debug|Win32"
+ OutputDirectory="Debug"
+ IntermediateDirectory="Debug"
+ ConfigurationType="4"
+ >
+ <Tool
+ Name="VCPreBuildEventTool"
+ />
+ <Tool
+ Name="VCCustomBuildTool"
+ />
+ <Tool
+ Name="VCXMLDataGeneratorTool"
+ />
+ <Tool
+ Name="VCWebServiceProxyGeneratorTool"
+ />
+ <Tool
+ Name="VCMIDLTool"
+ />
+ <Tool
+ Name="VCCLCompilerTool"
+ Optimization="0"
+ PreprocessorDefinitions="WIN32;_DEBUG;_CONSOLE"
+ MinimalRebuild="true"
+ BasicRuntimeChecks="3"
+ RuntimeLibrary="3"
+ UsePrecompiledHeader="0"
+ WarningLevel="3"
+ Detect64BitPortabilityProblems="true"
+ DebugInformationFormat="4"
+ CompileAs="1"
+ />
+ <Tool
+ Name="VCManagedResourceCompilerTool"
+ />
+ <Tool
+ Name="VCResourceCompilerTool"
+ />
+ <Tool
+ Name="VCPreLinkEventTool"
+ />
+ <Tool
+ Name="VCLibrarianTool"
+ />
+ <Tool
+ Name="VCALinkTool"
+ />
+ <Tool
+ Name="VCXDCMakeTool"
+ />
+ <Tool
+ Name="VCBscMakeTool"
+ />
+ <Tool
+ Name="VCFxCopTool"
+ />
+ <Tool
+ Name="VCPostBuildEventTool"
+ />
+ </Configuration>
+ <Configuration
+ Name="Release|Win32"
+ OutputDirectory="Release"
+ IntermediateDirectory="Release"
+ ConfigurationType="4"
+ >
+ <Tool
+ Name="VCPreBuildEventTool"
+ />
+ <Tool
+ Name="VCCustomBuildTool"
+ />
+ <Tool
+ Name="VCXMLDataGeneratorTool"
+ />
+ <Tool
+ Name="VCWebServiceProxyGeneratorTool"
+ />
+ <Tool
+ Name="VCMIDLTool"
+ />
+ <Tool
+ Name="VCCLCompilerTool"
+ PreprocessorDefinitions="WIN32;NDEBUG;_CONSOLE"
+ RuntimeLibrary="2"
+ UsePrecompiledHeader="0"
+ WarningLevel="3"
+ Detect64BitPortabilityProblems="true"
+ DebugInformationFormat="3"
+ CompileAs="1"
+ />
+ <Tool
+ Name="VCManagedResourceCompilerTool"
+ />
+ <Tool
+ Name="VCResourceCompilerTool"
+ />
+ <Tool
+ Name="VCPreLinkEventTool"
+ />
+ <Tool
+ Name="VCLibrarianTool"
+ />
+ <Tool
+ Name="VCALinkTool"
+ />
+ <Tool
+ Name="VCXDCMakeTool"
+ />
+ <Tool
+ Name="VCBscMakeTool"
+ />
+ <Tool
+ Name="VCFxCopTool"
+ />
+ <Tool
+ Name="VCPostBuildEventTool"
+ />
+ </Configuration>
+ </Configurations>
+ <References>
+ </References>
+ <Files>
+ <Filter
+ Name="Header Files"
+ Filter="h;hpp;hxx;hm;inl;inc;xsd"
+ >
+ <File
+ RelativePath=".\compiler.h"
+ >
+ </File>
+ <File
+ RelativePath=".\levmar.h"
+ >
+ </File>
+ <File
+ RelativePath=".\misc.h"
+ >
+ </File>
+ </Filter>
+ <Filter
+ Name="Resource Files"
+ Filter="rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx"
+ >
+ </Filter>
+ <Filter
+ Name="Source Files"
+ Filter="cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx"
+ >
+ <File
+ RelativePath=".\Axb.c"
+ >
+ </File>
+ <File
+ RelativePath=".\lm.c"
+ >
+ </File>
+ <File
+ RelativePath=".\lmbc.c"
+ >
+ </File>
+ <File
+ RelativePath=".\lmblec.c"
+ >
+ </File>
+ <File
+ RelativePath=".\lmlec.c"
+ >
+ </File>
+ <File
+ RelativePath=".\misc.c"
+ >
+ </File>
+ <File
+ RelativePath=".\lmbleic.c"
+ >
+ </File>
+ </Filter>
+ </Files>
+ <Globals>
+ </Globals>
+</VisualStudioProject>
diff --git a/sci-libs/levmar/levmar-2.5/liblevmar.a b/sci-libs/levmar/levmar-2.5/liblevmar.a
new file mode 100644
index 000000000..8ec604258
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/liblevmar.a
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/lm.c b/sci-libs/levmar/levmar-2.5/lm.c
new file mode 100644
index 000000000..81d8acb24
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lm.c
@@ -0,0 +1,83 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/********************************************************************************
+ * Levenberg-Marquardt nonlinear minimization. The same core code is used with
+ * appropriate #defines to derive single and double precision versions, see
+ * also lm_core.c
+ ********************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <float.h>
+
+#include "levmar.h"
+#include "compiler.h"
+#include "misc.h"
+
+#define EPSILON 1E-12
+#define ONE_THIRD 0.3333333334 /* 1.0/3.0 */
+
+#if !defined(LM_DBL_PREC) && !defined(LM_SNGL_PREC)
+#error At least one of LM_DBL_PREC, LM_SNGL_PREC should be defined!
+#endif
+
+
+#ifdef LM_SNGL_PREC
+/* single precision (float) definitions */
+#define LM_REAL float
+#define LM_PREFIX s
+
+#define LM_REAL_MAX FLT_MAX
+#define LM_REAL_MIN -FLT_MAX
+#define LM_REAL_EPSILON FLT_EPSILON
+#define __SUBCNST(x) x##F
+#define LM_CNST(x) __SUBCNST(x) // force substitution
+
+#include "lm_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_EPSILON
+#undef LM_REAL_MIN
+#undef __SUBCNST
+#undef LM_CNST
+#endif /* LM_SNGL_PREC */
+
+#ifdef LM_DBL_PREC
+/* double precision definitions */
+#define LM_REAL double
+#define LM_PREFIX d
+
+#define LM_REAL_MAX DBL_MAX
+#define LM_REAL_MIN -DBL_MAX
+#define LM_REAL_EPSILON DBL_EPSILON
+#define LM_CNST(x) (x)
+
+#include "lm_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_EPSILON
+#undef LM_REAL_MIN
+#undef LM_CNST
+#endif /* LM_DBL_PREC */
diff --git a/sci-libs/levmar/levmar-2.5/lm.h b/sci-libs/levmar/levmar-2.5/lm.h
new file mode 120000
index 000000000..980d00cd7
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lm.h
@@ -0,0 +1 @@
+levmar.h \ No newline at end of file
diff --git a/sci-libs/levmar/levmar-2.5/lm.o b/sci-libs/levmar/levmar-2.5/lm.o
new file mode 100644
index 000000000..3075b39cb
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lm.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/lm_core.c b/sci-libs/levmar/levmar-2.5/lm_core.c
new file mode 100644
index 000000000..d06df2c96
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lm_core.c
@@ -0,0 +1,843 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+#ifndef LM_REAL // not included by lm.c
+#error This file should not be compiled directly!
+#endif
+
+
+/* precision-specific definitions */
+#define LEVMAR_DER LM_ADD_PREFIX(levmar_der)
+#define LEVMAR_DIF LM_ADD_PREFIX(levmar_dif)
+#define LEVMAR_FDIF_FORW_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_forw_jac_approx)
+#define LEVMAR_FDIF_CENT_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_cent_jac_approx)
+#define LEVMAR_TRANS_MAT_MAT_MULT LM_ADD_PREFIX(levmar_trans_mat_mat_mult)
+#define LEVMAR_L2NRMXMY LM_ADD_PREFIX(levmar_L2nrmxmy)
+#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
+
+#ifdef HAVE_LAPACK
+#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU)
+#define AX_EQ_B_CHOL LM_ADD_PREFIX(Ax_eq_b_Chol)
+#define AX_EQ_B_QR LM_ADD_PREFIX(Ax_eq_b_QR)
+#define AX_EQ_B_QRLS LM_ADD_PREFIX(Ax_eq_b_QRLS)
+#define AX_EQ_B_SVD LM_ADD_PREFIX(Ax_eq_b_SVD)
+#define AX_EQ_B_BK LM_ADD_PREFIX(Ax_eq_b_BK)
+#else
+#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU_noLapack)
+#endif /* HAVE_LAPACK */
+
+/*
+ * This function seeks the parameter vector p that best describes the measurements vector x.
+ * More precisely, given a vector function func : R^m --> R^n with n>=m,
+ * it finds p s.t. func(p) ~= x, i.e. the squared second order (i.e. L2) norm of
+ * e=x-func(p) is minimized.
+ *
+ * This function requires an analytic Jacobian. In case the latter is unavailable,
+ * use LEVMAR_DIF() bellow
+ *
+ * Returns the number of iterations (>=0) if successful, LM_ERROR if failed
+ *
+ * For more details, see K. Madsen, H.B. Nielsen and O. Tingleff's lecture notes on
+ * non-linear least squares at http://www.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf
+ */
+
+int LEVMAR_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata), /* function to evaluate the Jacobian \part x / \part p */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[4], /* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3]. Respectively the scale factor for initial \mu,
+ * stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2. Set to NULL for defaults to be used
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_DER_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func & jacf.
+ * Set to NULL if not needed
+ */
+{
+register int i, j, k, l;
+int worksz, freework=0, issolved;
+/* temp work arrays */
+LM_REAL *e, /* nx1 */
+ *hx, /* \hat{x}_i, nx1 */
+ *jacTe, /* J^T e_i mx1 */
+ *jac, /* nxm */
+ *jacTjac, /* mxm */
+ *Dp, /* mx1 */
+ *diag_jacTjac, /* diagonal of J^T J, mx1 */
+ *pDp; /* p + Dp, mx1 */
+
+register LM_REAL mu, /* damping constant */
+ tmp; /* mainly used in matrix & vector multiplications */
+LM_REAL p_eL2, jacTe_inf, pDp_eL2; /* ||e(p)||_2, ||J^T e||_inf, ||e(p+Dp)||_2 */
+LM_REAL p_L2, Dp_L2=LM_REAL_MAX, dF, dL;
+LM_REAL tau, eps1, eps2, eps2_sq, eps3;
+LM_REAL init_p_eL2;
+int nu=2, nu2, stop=0, nfev, njev=0, nlss=0;
+const int nm=n*m;
+int (*linsolver)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)=NULL;
+
+ mu=jacTe_inf=0.0; /* -Wall */
+
+ if(n<m){
+ fprintf(stderr, LCAT(LEVMAR_DER, "(): cannot solve a problem with fewer measurements [%d] than unknowns [%d]\n"), n, m);
+ return LM_ERROR;
+ }
+
+ if(!jacf){
+ fprintf(stderr, RCAT("No function specified for computing the Jacobian in ", LEVMAR_DER)
+ RCAT("().\nIf no such function is available, use ", LEVMAR_DIF) RCAT("() rather than ", LEVMAR_DER) "()\n");
+ return LM_ERROR;
+ }
+
+ if(opts){
+ tau=opts[0];
+ eps1=opts[1];
+ eps2=opts[2];
+ eps2_sq=opts[2]*opts[2];
+ eps3=opts[3];
+ }
+ else{ // use default values
+ tau=LM_CNST(LM_INIT_MU);
+ eps1=LM_CNST(LM_STOP_THRESH);
+ eps2=LM_CNST(LM_STOP_THRESH);
+ eps2_sq=LM_CNST(LM_STOP_THRESH)*LM_CNST(LM_STOP_THRESH);
+ eps3=LM_CNST(LM_STOP_THRESH);
+ }
+
+ if(!work){
+ worksz=LM_DER_WORKSZ(m, n); //2*n+4*m + n*m + m*m;
+ work=(LM_REAL *)malloc(worksz*sizeof(LM_REAL)); /* allocate a big chunk in one step */
+ if(!work){
+ fprintf(stderr, LCAT(LEVMAR_DER, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ freework=1;
+ }
+
+ /* set up work arrays */
+ e=work;
+ hx=e + n;
+ jacTe=hx + n;
+ jac=jacTe + m;
+ jacTjac=jac + nm;
+ Dp=jacTjac + m*m;
+ diag_jacTjac=Dp + m;
+ pDp=diag_jacTjac + m;
+
+ /* compute e=x - f(p) and its L2 norm */
+ (*func)(p, hx, m, n, adata); nfev=1;
+ /* ### e=x-hx, p_eL2=||e|| */
+#if 1
+ p_eL2=LEVMAR_L2NRMXMY(e, x, hx, n);
+#else
+ for(i=0, p_eL2=0.0; i<n; ++i){
+ e[i]=tmp=x[i]-hx[i];
+ p_eL2+=tmp*tmp;
+ }
+#endif
+ init_p_eL2=p_eL2;
+ if(!LM_FINITE(p_eL2)) stop=7;
+
+ for(k=0; k<itmax && !stop; ++k){
+ /* Note that p and e have been updated at a previous iteration */
+
+ if(p_eL2<=eps3){ /* error is small */
+ stop=6;
+ break;
+ }
+
+ /* Compute the Jacobian J at p, J^T J, J^T e, ||J^T e||_inf and ||p||^2.
+ * Since J^T J is symmetric, its computation can be sped up by computing
+ * only its upper triangular part and copying it to the lower part
+ */
+
+ (*jacf)(p, jac, m, n, adata); ++njev;
+
+ /* J^T J, J^T e */
+ if(nm<__BLOCKSZ__SQ){ // this is a small problem
+ /* J^T*J_ij = \sum_l J^T_il * J_lj = \sum_l J_li * J_lj.
+ * Thus, the product J^T J can be computed using an outer loop for
+ * l that adds J_li*J_lj to each element ij of the result. Note that
+ * with this scheme, the accesses to J and JtJ are always along rows,
+ * therefore induces less cache misses compared to the straightforward
+ * algorithm for computing the product (i.e., l loop is innermost one).
+ * A similar scheme applies to the computation of J^T e.
+ * However, for large minimization problems (i.e., involving a large number
+ * of unknowns and measurements) for which J/J^T J rows are too large to
+ * fit in the L1 cache, even this scheme incures many cache misses. In
+ * such cases, a cache-efficient blocking scheme is preferable.
+ *
+ * Thanks to John Nitao of Lawrence Livermore Lab for pointing out this
+ * performance problem.
+ *
+ * Note that the non-blocking algorithm is faster on small
+ * problems since in this case it avoids the overheads of blocking.
+ */
+
+ /* looping downwards saves a few computations */
+ register int l, im;
+ register LM_REAL alpha, *jaclm;
+
+ for(i=m*m; i-->0; )
+ jacTjac[i]=0.0;
+ for(i=m; i-->0; )
+ jacTe[i]=0.0;
+
+ for(l=n; l-->0; ){
+ jaclm=jac+l*m;
+ for(i=m; i-->0; ){
+ im=i*m;
+ alpha=jaclm[i]; //jac[l*m+i];
+ for(j=i+1; j-->0; ) /* j<=i computes lower triangular part only */
+ jacTjac[im+j]+=jaclm[j]*alpha; //jac[l*m+j]
+
+ /* J^T e */
+ jacTe[i]+=alpha*e[l];
+ }
+ }
+
+ for(i=m; i-->0; ) /* copy to upper part */
+ for(j=i+1; j<m; ++j)
+ jacTjac[i*m+j]=jacTjac[j*m+i];
+
+ }
+ else{ // this is a large problem
+ /* Cache efficient computation of J^T J based on blocking
+ */
+ LEVMAR_TRANS_MAT_MAT_MULT(jac, jacTjac, n, m);
+
+ /* cache efficient computation of J^T e */
+ for(i=0; i<m; ++i)
+ jacTe[i]=0.0;
+
+ for(i=0; i<n; ++i){
+ register LM_REAL *jacrow;
+
+ for(l=0, jacrow=jac+i*m, tmp=e[i]; l<m; ++l)
+ jacTe[l]+=jacrow[l]*tmp;
+ }
+ }
+
+ /* Compute ||J^T e||_inf and ||p||^2 */
+ for(i=0, p_L2=jacTe_inf=0.0; i<m; ++i){
+ if(jacTe_inf < (tmp=FABS(jacTe[i]))) jacTe_inf=tmp;
+
+ diag_jacTjac[i]=jacTjac[i*m+i]; /* save diagonal entries so that augmentation can be later canceled */
+ p_L2+=p[i]*p[i];
+ }
+ //p_L2=sqrt(p_L2);
+
+#if 0
+if(!(k%100)){
+ printf("Current estimate: ");
+ for(i=0; i<m; ++i)
+ printf("%.9g ", p[i]);
+ printf("-- errors %.9g %0.9g\n", jacTe_inf, p_eL2);
+}
+#endif
+
+ /* check for convergence */
+ if((jacTe_inf <= eps1)){
+ Dp_L2=0.0; /* no increment for p in this case */
+ stop=1;
+ break;
+ }
+
+ /* compute initial damping factor */
+ if(k==0){
+ for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
+ if(diag_jacTjac[i]>tmp) tmp=diag_jacTjac[i]; /* find max diagonal element */
+ mu=tau*tmp;
+ }
+
+ /* determine increment using adaptive damping */
+ while(1){
+ /* augment normal equations */
+ for(i=0; i<m; ++i)
+ jacTjac[i*m+i]+=mu;
+
+ /* solve augmented equations */
+#ifdef HAVE_LAPACK
+ /* 6 alternatives are available: LU, Cholesky, 2 variants of QR decomposition, SVD and LDLt.
+ * Cholesky is the fastest but might be inaccurate; QR is slower but more accurate;
+ * SVD is the slowest but most accurate; LU offers a tradeoff between accuracy and speed
+ */
+
+ issolved=AX_EQ_B_BK(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_BK;
+ //issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
+ //issolved=AX_EQ_B_CHOL(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_CHOL;
+ //issolved=AX_EQ_B_QR(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_QR;
+ //issolved=AX_EQ_B_QRLS(jacTjac, jacTe, Dp, m, m); ++nlss; linsolver=(int (*)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m))AX_EQ_B_QRLS;
+ //issolved=AX_EQ_B_SVD(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_SVD;
+
+#else
+ /* use the LU included with levmar */
+ issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
+#endif /* HAVE_LAPACK */
+
+ if(issolved){
+ /* compute p's new estimate and ||Dp||^2 */
+ for(i=0, Dp_L2=0.0; i<m; ++i){
+ pDp[i]=p[i] + (tmp=Dp[i]);
+ Dp_L2+=tmp*tmp;
+ }
+ //Dp_L2=sqrt(Dp_L2);
+
+ if(Dp_L2<=eps2_sq*p_L2){ /* relative change in p is small, stop */
+ //if(Dp_L2<=eps2*(p_L2 + eps2)){ /* relative change in p is small, stop */
+ stop=2;
+ break;
+ }
+
+ if(Dp_L2>=(p_L2+eps2)/(LM_CNST(EPSILON)*LM_CNST(EPSILON))){ /* almost singular */
+ //if(Dp_L2>=(p_L2+eps2)/LM_CNST(EPSILON)){ /* almost singular */
+ stop=4;
+ break;
+ }
+
+ (*func)(pDp, hx, m, n, adata); ++nfev; /* evaluate function at p + Dp */
+ /* compute ||e(pDp)||_2 */
+ /* ### hx=x-hx, pDp_eL2=||hx|| */
+#if 1
+ pDp_eL2=LEVMAR_L2NRMXMY(hx, x, hx, n);
+#else
+ for(i=0, pDp_eL2=0.0; i<n; ++i){
+ hx[i]=tmp=x[i]-hx[i];
+ pDp_eL2+=tmp*tmp;
+ }
+#endif
+ if(!LM_FINITE(pDp_eL2)){ /* sum of squares is not finite, most probably due to a user error.
+ * This check makes sure that the inner loop does not run indefinitely.
+ * Thanks to Steve Danauskas for reporting such cases
+ */
+ stop=7;
+ break;
+ }
+
+ for(i=0, dL=0.0; i<m; ++i)
+ dL+=Dp[i]*(mu*Dp[i]+jacTe[i]);
+
+ dF=p_eL2-pDp_eL2;
+
+ if(dL>0.0 && dF>0.0){ /* reduction in error, increment is accepted */
+ tmp=(LM_CNST(2.0)*dF/dL-LM_CNST(1.0));
+ tmp=LM_CNST(1.0)-tmp*tmp*tmp;
+ mu=mu*( (tmp>=LM_CNST(ONE_THIRD))? tmp : LM_CNST(ONE_THIRD) );
+ nu=2;
+
+ for(i=0 ; i<m; ++i) /* update p's estimate */
+ p[i]=pDp[i];
+
+ for(i=0; i<n; ++i) /* update e and ||e||_2 */
+ e[i]=hx[i];
+ p_eL2=pDp_eL2;
+ break;
+ }
+ }
+
+ /* if this point is reached, either the linear system could not be solved or
+ * the error did not reduce; in any case, the increment must be rejected
+ */
+
+ mu*=nu;
+ nu2=nu<<1; // 2*nu;
+ if(nu2<=nu){ /* nu has wrapped around (overflown). Thanks to Frank Jordan for spotting this case */
+ stop=5;
+ break;
+ }
+ nu=nu2;
+
+ for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
+ jacTjac[i*m+i]=diag_jacTjac[i];
+ } /* inner loop */
+ }
+
+ if(k>=itmax) stop=3;
+
+ for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
+ jacTjac[i*m+i]=diag_jacTjac[i];
+
+ if(info){
+ info[0]=init_p_eL2;
+ info[1]=p_eL2;
+ info[2]=jacTe_inf;
+ info[3]=Dp_L2;
+ for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
+ if(tmp<jacTjac[i*m+i]) tmp=jacTjac[i*m+i];
+ info[4]=mu/tmp;
+ info[5]=(LM_REAL)k;
+ info[6]=(LM_REAL)stop;
+ info[7]=(LM_REAL)nfev;
+ info[8]=(LM_REAL)njev;
+ info[9]=(LM_REAL)nlss;
+ }
+
+ /* covariance matrix */
+ if(covar){
+ LEVMAR_COVAR(jacTjac, covar, p_eL2, m, n);
+ }
+
+ if(freework) free(work);
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(linsolver) (*linsolver)(NULL, NULL, NULL, 0);
+#endif
+
+ return (stop!=4 && stop!=7)? k : LM_ERROR;
+}
+
+
+/* Secant version of the LEVMAR_DER() function above: the Jacobian is approximated with
+ * the aid of finite differences (forward or central, see the comment for the opts argument)
+ */
+int LEVMAR_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[5], /* I: opts[0-4] = minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \delta]. Respectively the
+ * scale factor for initial \mu, stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2 and
+ * the step used in difference approximation to the Jacobian. Set to NULL for defaults to be used.
+ * If \delta<0, the Jacobian is approximated with central differences which are more accurate
+ * (but slower!) compared to the forward differences employed by default.
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_DIF_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func.
+ * Set to NULL if not needed
+ */
+{
+register int i, j, k, l;
+int worksz, freework=0, issolved;
+/* temp work arrays */
+LM_REAL *e, /* nx1 */
+ *hx, /* \hat{x}_i, nx1 */
+ *jacTe, /* J^T e_i mx1 */
+ *jac, /* nxm */
+ *jacTjac, /* mxm */
+ *Dp, /* mx1 */
+ *diag_jacTjac, /* diagonal of J^T J, mx1 */
+ *pDp, /* p + Dp, mx1 */
+ *wrk, /* nx1 */
+ *wrk2; /* nx1, used only for holding a temporary e vector and when differentiating with central differences */
+
+int using_ffdif=1;
+
+register LM_REAL mu, /* damping constant */
+ tmp; /* mainly used in matrix & vector multiplications */
+LM_REAL p_eL2, jacTe_inf, pDp_eL2; /* ||e(p)||_2, ||J^T e||_inf, ||e(p+Dp)||_2 */
+LM_REAL p_L2, Dp_L2=LM_REAL_MAX, dF, dL;
+LM_REAL tau, eps1, eps2, eps2_sq, eps3, delta;
+LM_REAL init_p_eL2;
+int nu, nu2, stop=0, nfev, njap=0, nlss=0, K=(m>=10)? m: 10, updjac, updp=1, newjac;
+const int nm=n*m;
+int (*linsolver)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)=NULL;
+
+ mu=jacTe_inf=p_L2=0.0; /* -Wall */
+ updjac=newjac=0; /* -Wall */
+
+ if(n<m){
+ fprintf(stderr, LCAT(LEVMAR_DIF, "(): cannot solve a problem with fewer measurements [%d] than unknowns [%d]\n"), n, m);
+ return LM_ERROR;
+ }
+
+ if(opts){
+ tau=opts[0];
+ eps1=opts[1];
+ eps2=opts[2];
+ eps2_sq=opts[2]*opts[2];
+ eps3=opts[3];
+ delta=opts[4];
+ if(delta<0.0){
+ delta=-delta; /* make positive */
+ using_ffdif=0; /* use central differencing */
+ }
+ }
+ else{ // use default values
+ tau=LM_CNST(LM_INIT_MU);
+ eps1=LM_CNST(LM_STOP_THRESH);
+ eps2=LM_CNST(LM_STOP_THRESH);
+ eps2_sq=LM_CNST(LM_STOP_THRESH)*LM_CNST(LM_STOP_THRESH);
+ eps3=LM_CNST(LM_STOP_THRESH);
+ delta=LM_CNST(LM_DIFF_DELTA);
+ }
+
+ if(!work){
+ worksz=LM_DIF_WORKSZ(m, n); //4*n+4*m + n*m + m*m;
+ work=(LM_REAL *)malloc(worksz*sizeof(LM_REAL)); /* allocate a big chunk in one step */
+ if(!work){
+ fprintf(stderr, LCAT(LEVMAR_DIF, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ freework=1;
+ }
+
+ /* set up work arrays */
+ e=work;
+ hx=e + n;
+ jacTe=hx + n;
+ jac=jacTe + m;
+ jacTjac=jac + nm;
+ Dp=jacTjac + m*m;
+ diag_jacTjac=Dp + m;
+ pDp=diag_jacTjac + m;
+ wrk=pDp + m;
+ wrk2=wrk + n;
+
+ /* compute e=x - f(p) and its L2 norm */
+ (*func)(p, hx, m, n, adata); nfev=1;
+ /* ### e=x-hx, p_eL2=||e|| */
+#if 1
+ p_eL2=LEVMAR_L2NRMXMY(e, x, hx, n);
+#else
+ for(i=0, p_eL2=0.0; i<n; ++i){
+ e[i]=tmp=x[i]-hx[i];
+ p_eL2+=tmp*tmp;
+ }
+#endif
+ init_p_eL2=p_eL2;
+ if(!LM_FINITE(p_eL2)) stop=7;
+
+ nu=20; /* force computation of J */
+
+ for(k=0; k<itmax && !stop; ++k){
+ /* Note that p and e have been updated at a previous iteration */
+
+ if(p_eL2<=eps3){ /* error is small */
+ stop=6;
+ break;
+ }
+
+ /* Compute the Jacobian J at p, J^T J, J^T e, ||J^T e||_inf and ||p||^2.
+ * The symmetry of J^T J is again exploited for speed
+ */
+
+ if((updp && nu>16) || updjac==K){ /* compute difference approximation to J */
+ if(using_ffdif){ /* use forward differences */
+ LEVMAR_FDIF_FORW_JAC_APPROX(func, p, hx, wrk, delta, jac, m, n, adata);
+ ++njap; nfev+=m;
+ }
+ else{ /* use central differences */
+ LEVMAR_FDIF_CENT_JAC_APPROX(func, p, wrk, wrk2, delta, jac, m, n, adata);
+ ++njap; nfev+=2*m;
+ }
+ nu=2; updjac=0; updp=0; newjac=1;
+ }
+
+ if(newjac){ /* Jacobian has changed, recompute J^T J, J^t e, etc */
+ newjac=0;
+
+ /* J^T J, J^T e */
+ if(nm<=__BLOCKSZ__SQ){ // this is a small problem
+ /* J^T*J_ij = \sum_l J^T_il * J_lj = \sum_l J_li * J_lj.
+ * Thus, the product J^T J can be computed using an outer loop for
+ * l that adds J_li*J_lj to each element ij of the result. Note that
+ * with this scheme, the accesses to J and JtJ are always along rows,
+ * therefore induces less cache misses compared to the straightforward
+ * algorithm for computing the product (i.e., l loop is innermost one).
+ * A similar scheme applies to the computation of J^T e.
+ * However, for large minimization problems (i.e., involving a large number
+ * of unknowns and measurements) for which J/J^T J rows are too large to
+ * fit in the L1 cache, even this scheme incures many cache misses. In
+ * such cases, a cache-efficient blocking scheme is preferable.
+ *
+ * Thanks to John Nitao of Lawrence Livermore Lab for pointing out this
+ * performance problem.
+ *
+ * Note that the non-blocking algorithm is faster on small
+ * problems since in this case it avoids the overheads of blocking.
+ */
+ register int l, im;
+ register LM_REAL alpha, *jaclm;
+
+ /* looping downwards saves a few computations */
+ for(i=m*m; i-->0; )
+ jacTjac[i]=0.0;
+ for(i=m; i-->0; )
+ jacTe[i]=0.0;
+
+ for(l=n; l-->0; ){
+ jaclm=jac+l*m;
+ for(i=m; i-->0; ){
+ im=i*m;
+ alpha=jaclm[i]; //jac[l*m+i];
+ for(j=i+1; j-->0; ) /* j<=i computes lower triangular part only */
+ jacTjac[im+j]+=jaclm[j]*alpha; //jac[l*m+j]
+
+ /* J^T e */
+ jacTe[i]+=alpha*e[l];
+ }
+ }
+
+ for(i=m; i-->0; ) /* copy to upper part */
+ for(j=i+1; j<m; ++j)
+ jacTjac[i*m+j]=jacTjac[j*m+i];
+ }
+ else{ // this is a large problem
+ /* Cache efficient computation of J^T J based on blocking
+ */
+ LEVMAR_TRANS_MAT_MAT_MULT(jac, jacTjac, n, m);
+
+ /* cache efficient computation of J^T e */
+ for(i=0; i<m; ++i)
+ jacTe[i]=0.0;
+
+ for(i=0; i<n; ++i){
+ register LM_REAL *jacrow;
+
+ for(l=0, jacrow=jac+i*m, tmp=e[i]; l<m; ++l)
+ jacTe[l]+=jacrow[l]*tmp;
+ }
+ }
+
+ /* Compute ||J^T e||_inf and ||p||^2 */
+ for(i=0, p_L2=jacTe_inf=0.0; i<m; ++i){
+ if(jacTe_inf < (tmp=FABS(jacTe[i]))) jacTe_inf=tmp;
+
+ diag_jacTjac[i]=jacTjac[i*m+i]; /* save diagonal entries so that augmentation can be later canceled */
+ p_L2+=p[i]*p[i];
+ }
+ //p_L2=sqrt(p_L2);
+ }
+
+#if 0
+if(!(k%100)){
+ printf("Current estimate: ");
+ for(i=0; i<m; ++i)
+ printf("%.9g ", p[i]);
+ printf("-- errors %.9g %0.9g\n", jacTe_inf, p_eL2);
+}
+#endif
+
+ /* check for convergence */
+ if((jacTe_inf <= eps1)){
+ Dp_L2=0.0; /* no increment for p in this case */
+ stop=1;
+ break;
+ }
+
+ /* compute initial damping factor */
+ if(k==0){
+ for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
+ if(diag_jacTjac[i]>tmp) tmp=diag_jacTjac[i]; /* find max diagonal element */
+ mu=tau*tmp;
+ }
+
+ /* determine increment using adaptive damping */
+
+ /* augment normal equations */
+ for(i=0; i<m; ++i)
+ jacTjac[i*m+i]+=mu;
+
+ /* solve augmented equations */
+#ifdef HAVE_LAPACK
+ /* 6 alternatives are available: LU, Cholesky, 2 variants of QR decomposition, SVD and LDLt.
+ * Cholesky is the fastest but might be inaccurate; QR is slower but more accurate;
+ * SVD is the slowest but most accurate; LU offers a tradeoff between accuracy and speed
+ */
+
+ issolved=AX_EQ_B_BK(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_BK;
+ //issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
+ //issolved=AX_EQ_B_CHOL(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_CHOL;
+ //issolved=AX_EQ_B_QR(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_QR;
+ //issolved=AX_EQ_B_QRLS(jacTjac, jacTe, Dp, m, m); ++nlss; linsolver=(int (*)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m))AX_EQ_B_QRLS;
+ //issolved=AX_EQ_B_SVD(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_SVD;
+#else
+ /* use the LU included with levmar */
+ issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
+#endif /* HAVE_LAPACK */
+
+ if(issolved){
+ /* compute p's new estimate and ||Dp||^2 */
+ for(i=0, Dp_L2=0.0; i<m; ++i){
+ pDp[i]=p[i] + (tmp=Dp[i]);
+ Dp_L2+=tmp*tmp;
+ }
+ //Dp_L2=sqrt(Dp_L2);
+
+ if(Dp_L2<=eps2_sq*p_L2){ /* relative change in p is small, stop */
+ //if(Dp_L2<=eps2*(p_L2 + eps2)){ /* relative change in p is small, stop */
+ stop=2;
+ break;
+ }
+
+ if(Dp_L2>=(p_L2+eps2)/(LM_CNST(EPSILON)*LM_CNST(EPSILON))){ /* almost singular */
+ //if(Dp_L2>=(p_L2+eps2)/LM_CNST(EPSILON)){ /* almost singular */
+ stop=4;
+ break;
+ }
+
+ (*func)(pDp, wrk, m, n, adata); ++nfev; /* evaluate function at p + Dp */
+ /* compute ||e(pDp)||_2 */
+ /* ### wrk2=x-wrk, pDp_eL2=||wrk2|| */
+#if 1
+ pDp_eL2=LEVMAR_L2NRMXMY(wrk2, x, wrk, n);
+#else
+ for(i=0, pDp_eL2=0.0; i<n; ++i){
+ wrk2[i]=tmp=x[i]-wrk[i];
+ pDp_eL2+=tmp*tmp;
+ }
+#endif
+ if(!LM_FINITE(pDp_eL2)){ /* sum of squares is not finite, most probably due to a user error.
+ * This check makes sure that the loop terminates early in the case
+ * of invalid input. Thanks to Steve Danauskas for suggesting it
+ */
+
+ stop=7;
+ break;
+ }
+
+ dF=p_eL2-pDp_eL2;
+ if(updp || dF>0){ /* update jac */
+ for(i=0; i<n; ++i){
+ for(l=0, tmp=0.0; l<m; ++l)
+ tmp+=jac[i*m+l]*Dp[l]; /* (J * Dp)[i] */
+ tmp=(wrk[i] - hx[i] - tmp)/Dp_L2; /* (f(p+dp)[i] - f(p)[i] - (J * Dp)[i])/(dp^T*dp) */
+ for(j=0; j<m; ++j)
+ jac[i*m+j]+=tmp*Dp[j];
+ }
+ ++updjac;
+ newjac=1;
+ }
+
+ for(i=0, dL=0.0; i<m; ++i)
+ dL+=Dp[i]*(mu*Dp[i]+jacTe[i]);
+
+ if(dL>0.0 && dF>0.0){ /* reduction in error, increment is accepted */
+ tmp=(LM_CNST(2.0)*dF/dL-LM_CNST(1.0));
+ tmp=LM_CNST(1.0)-tmp*tmp*tmp;
+ mu=mu*( (tmp>=LM_CNST(ONE_THIRD))? tmp : LM_CNST(ONE_THIRD) );
+ nu=2;
+
+ for(i=0 ; i<m; ++i) /* update p's estimate */
+ p[i]=pDp[i];
+
+ for(i=0; i<n; ++i){ /* update e, hx and ||e||_2 */
+ e[i]=wrk2[i]; //x[i]-wrk[i];
+ hx[i]=wrk[i];
+ }
+ p_eL2=pDp_eL2;
+ updp=1;
+ continue;
+ }
+ }
+
+ /* if this point is reached, either the linear system could not be solved or
+ * the error did not reduce; in any case, the increment must be rejected
+ */
+
+ mu*=nu;
+ nu2=nu<<1; // 2*nu;
+ if(nu2<=nu){ /* nu has wrapped around (overflown). Thanks to Frank Jordan for spotting this case */
+ stop=5;
+ break;
+ }
+ nu=nu2;
+
+ for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
+ jacTjac[i*m+i]=diag_jacTjac[i];
+ }
+
+ if(k>=itmax) stop=3;
+
+ for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
+ jacTjac[i*m+i]=diag_jacTjac[i];
+
+ if(info){
+ info[0]=init_p_eL2;
+ info[1]=p_eL2;
+ info[2]=jacTe_inf;
+ info[3]=Dp_L2;
+ for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
+ if(tmp<jacTjac[i*m+i]) tmp=jacTjac[i*m+i];
+ info[4]=mu/tmp;
+ info[5]=(LM_REAL)k;
+ info[6]=(LM_REAL)stop;
+ info[7]=(LM_REAL)nfev;
+ info[8]=(LM_REAL)njap;
+ info[9]=(LM_REAL)nlss;
+ }
+
+ /* covariance matrix */
+ if(covar){
+ LEVMAR_COVAR(jacTjac, covar, p_eL2, m, n);
+ }
+
+
+ if(freework) free(work);
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(linsolver) (*linsolver)(NULL, NULL, NULL, 0);
+#endif
+
+ return (stop!=4 && stop!=7)? k : LM_ERROR;
+}
+
+/* undefine everything. THIS MUST REMAIN AT THE END OF THE FILE */
+#undef LEVMAR_DER
+#undef LEVMAR_DIF
+#undef LEVMAR_FDIF_FORW_JAC_APPROX
+#undef LEVMAR_FDIF_CENT_JAC_APPROX
+#undef LEVMAR_COVAR
+#undef LEVMAR_TRANS_MAT_MAT_MULT
+#undef LEVMAR_L2NRMXMY
+#undef AX_EQ_B_LU
+#undef AX_EQ_B_CHOL
+#undef AX_EQ_B_QR
+#undef AX_EQ_B_QRLS
+#undef AX_EQ_B_SVD
+#undef AX_EQ_B_BK
diff --git a/sci-libs/levmar/levmar-2.5/lmbc.c b/sci-libs/levmar/levmar-2.5/lmbc.c
new file mode 100644
index 000000000..17c4a2465
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmbc.c
@@ -0,0 +1,85 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-05 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/********************************************************************************
+ * Box-constrained Levenberg-Marquardt nonlinear minimization. The same core code
+ * is used with appropriate #defines to derive single and double precision versions,
+ * see also lmbc_core.c
+ ********************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <float.h>
+
+#include "levmar.h"
+#include "compiler.h"
+#include "misc.h"
+
+#define EPSILON 1E-12
+#define ONE_THIRD 0.3333333334 /* 1.0/3.0 */
+
+#if !defined(LM_DBL_PREC) && !defined(LM_SNGL_PREC)
+#error At least one of LM_DBL_PREC, LM_SNGL_PREC should be defined!
+#endif
+
+
+#ifdef LM_SNGL_PREC
+/* single precision (float) definitions */
+#define LM_REAL float
+#define LM_PREFIX s
+
+#define LM_REAL_MAX FLT_MAX
+#define LM_REAL_MIN -FLT_MAX
+
+#define LM_REAL_EPSILON FLT_EPSILON
+#define __SUBCNST(x) x##F
+#define LM_CNST(x) __SUBCNST(x) // force substitution
+
+#include "lmbc_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_MIN
+#undef LM_REAL_EPSILON
+#undef __SUBCNST
+#undef LM_CNST
+#endif /* LM_SNGL_PREC */
+
+#ifdef LM_DBL_PREC
+/* double precision definitions */
+#define LM_REAL double
+#define LM_PREFIX d
+
+#define LM_REAL_MAX DBL_MAX
+#define LM_REAL_MIN -DBL_MAX
+
+#define LM_REAL_EPSILON DBL_EPSILON
+#define LM_CNST(x) (x)
+
+#include "lmbc_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_MIN
+#undef LM_REAL_EPSILON
+#undef LM_CNST
+#endif /* LM_DBL_PREC */
diff --git a/sci-libs/levmar/levmar-2.5/lmbc.o b/sci-libs/levmar/levmar-2.5/lmbc.o
new file mode 100644
index 000000000..53b0e6599
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmbc.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/lmbc_core.c b/sci-libs/levmar/levmar-2.5/lmbc_core.c
new file mode 100644
index 000000000..ca0fdb054
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmbc_core.c
@@ -0,0 +1,948 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-05 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+#ifndef LM_REAL // not included by lmbc.c
+#error This file should not be compiled directly!
+#endif
+
+
+/* precision-specific definitions */
+#define FUNC_STATE LM_ADD_PREFIX(func_state)
+#define LNSRCH LM_ADD_PREFIX(lnsrch)
+#define BOXPROJECT LM_ADD_PREFIX(boxProject)
+#define LEVMAR_BOX_CHECK LM_ADD_PREFIX(levmar_box_check)
+#define LEVMAR_BC_DER LM_ADD_PREFIX(levmar_bc_der)
+#define LEVMAR_BC_DIF LM_ADD_PREFIX(levmar_bc_dif)
+#define LEVMAR_FDIF_FORW_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_forw_jac_approx)
+#define LEVMAR_FDIF_CENT_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_cent_jac_approx)
+#define LEVMAR_TRANS_MAT_MAT_MULT LM_ADD_PREFIX(levmar_trans_mat_mat_mult)
+#define LEVMAR_L2NRMXMY LM_ADD_PREFIX(levmar_L2nrmxmy)
+#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
+#define LMBC_DIF_DATA LM_ADD_PREFIX(lmbc_dif_data)
+#define LMBC_DIF_FUNC LM_ADD_PREFIX(lmbc_dif_func)
+#define LMBC_DIF_JACF LM_ADD_PREFIX(lmbc_dif_jacf)
+
+#ifdef HAVE_LAPACK
+#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU)
+#define AX_EQ_B_CHOL LM_ADD_PREFIX(Ax_eq_b_Chol)
+#define AX_EQ_B_QR LM_ADD_PREFIX(Ax_eq_b_QR)
+#define AX_EQ_B_QRLS LM_ADD_PREFIX(Ax_eq_b_QRLS)
+#define AX_EQ_B_SVD LM_ADD_PREFIX(Ax_eq_b_SVD)
+#define AX_EQ_B_BK LM_ADD_PREFIX(Ax_eq_b_BK)
+#else
+#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU_noLapack)
+#endif /* HAVE_LAPACK */
+
+/* find the median of 3 numbers */
+#define __MEDIAN3(a, b, c) ( ((a) >= (b))?\
+ ( ((c) >= (a))? (a) : ( ((c) <= (b))? (b) : (c) ) ) : \
+ ( ((c) >= (b))? (b) : ( ((c) <= (a))? (a) : (c) ) ) )
+
+#define _POW_ LM_CNST(2.1)
+
+#define __LSITMAX 150 // max #iterations for line search
+
+struct FUNC_STATE{
+ int n, *nfev;
+ LM_REAL *hx, *x;
+ void *adata;
+};
+
+static void
+LNSRCH(int m, LM_REAL *x, LM_REAL f, LM_REAL *g, LM_REAL *p, LM_REAL alpha, LM_REAL *xpls,
+ LM_REAL *ffpls, void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), struct FUNC_STATE state,
+ int *mxtake, int *iretcd, LM_REAL stepmx, LM_REAL steptl, LM_REAL *sx)
+{
+/* Find a next newton iterate by backtracking line search.
+ * Specifically, finds a \lambda such that for a fixed alpha<0.5 (usually 1e-4),
+ * f(x + \lambda*p) <= f(x) + alpha * \lambda * g^T*p
+ *
+ * Translated (with minor changes) from Schnabel, Koontz & Weiss uncmin.f, v1.3
+
+ * PARAMETERS :
+
+ * m --> dimension of problem (i.e. number of variables)
+ * x(m) --> old iterate: x[k-1]
+ * f --> function value at old iterate, f(x)
+ * g(m) --> gradient at old iterate, g(x), or approximate
+ * p(m) --> non-zero newton step
+ * alpha --> fixed constant < 0.5 for line search (see above)
+ * xpls(m) <-- new iterate x[k]
+ * ffpls <-- function value at new iterate, f(xpls)
+ * func --> name of subroutine to evaluate function
+ * state <--> information other than x and m that func requires.
+ * state is not modified in xlnsrch (but can be modified by func).
+ * iretcd <-- return code
+ * mxtake <-- boolean flag indicating step of maximum length used
+ * stepmx --> maximum allowable step size
+ * steptl --> relative step size at which successive iterates
+ * considered close enough to terminate algorithm
+ * sx(m) --> diagonal scaling matrix for x, can be NULL
+
+ * internal variables
+
+ * sln newton length
+ * rln relative length of newton step
+*/
+
+ register int i, j;
+ int firstback = 1;
+ LM_REAL disc;
+ LM_REAL a3, b;
+ LM_REAL t1, t2, t3, lambda, tlmbda, rmnlmb;
+ LM_REAL scl, rln, sln, slp;
+ LM_REAL tmp1, tmp2;
+ LM_REAL fpls, pfpls = 0., plmbda = 0.; /* -Wall */
+
+ f*=LM_CNST(0.5);
+ *mxtake = 0;
+ *iretcd = 2;
+ tmp1 = 0.;
+ if(!sx) /* no scaling */
+ for (i = 0; i < m; ++i)
+ tmp1 += p[i] * p[i];
+ else
+ for (i = 0; i < m; ++i)
+ tmp1 += sx[i] * sx[i] * p[i] * p[i];
+ sln = (LM_REAL)sqrt(tmp1);
+ if (sln > stepmx) {
+ /* newton step longer than maximum allowed */
+ scl = stepmx / sln;
+ for(i=0; i<m; ++i) /* p * scl */
+ p[i]*=scl;
+ sln = stepmx;
+ }
+ for(i=0, slp=0.; i<m; ++i) /* g^T * p */
+ slp+=g[i]*p[i];
+ rln = 0.;
+ if(!sx) /* no scaling */
+ for (i = 0; i < m; ++i) {
+ tmp1 = (FABS(x[i])>=LM_CNST(1.))? FABS(x[i]) : LM_CNST(1.);
+ tmp2 = FABS(p[i])/tmp1;
+ if(rln < tmp2) rln = tmp2;
+ }
+ else
+ for (i = 0; i < m; ++i) {
+ tmp1 = (FABS(x[i])>=LM_CNST(1.)/sx[i])? FABS(x[i]) : LM_CNST(1.)/sx[i];
+ tmp2 = FABS(p[i])/tmp1;
+ if(rln < tmp2) rln = tmp2;
+ }
+ rmnlmb = steptl / rln;
+ lambda = LM_CNST(1.0);
+
+ /* check if new iterate satisfactory. generate new lambda if necessary. */
+
+ for(j=__LSITMAX; j>=0; --j) {
+ for (i = 0; i < m; ++i)
+ xpls[i] = x[i] + lambda * p[i];
+
+ /* evaluate function at new point */
+ (*func)(xpls, state.hx, m, state.n, state.adata); ++(*(state.nfev));
+ /* ### state.hx=state.x-state.hx, tmp1=||state.hx|| */
+#if 1
+ tmp1=LEVMAR_L2NRMXMY(state.hx, state.x, state.hx, state.n);
+#else
+ for(i=0, tmp1=0.0; i<state.n; ++i){
+ state.hx[i]=tmp2=state.x[i]-state.hx[i];
+ tmp1+=tmp2*tmp2;
+ }
+#endif
+ fpls=LM_CNST(0.5)*tmp1; *ffpls=tmp1;
+
+ if (fpls <= f + slp * alpha * lambda) { /* solution found */
+ *iretcd = 0;
+ if (lambda == LM_CNST(1.) && sln > stepmx * LM_CNST(.99)) *mxtake = 1;
+ return;
+ }
+
+ /* else : solution not (yet) found */
+
+ /* First find a point with a finite value */
+
+ if (lambda < rmnlmb) {
+ /* no satisfactory xpls found sufficiently distinct from x */
+
+ *iretcd = 1;
+ return;
+ }
+ else { /* calculate new lambda */
+
+ /* modifications to cover non-finite values */
+ if (!LM_FINITE(fpls)) {
+ lambda *= LM_CNST(0.1);
+ firstback = 1;
+ }
+ else {
+ if (firstback) { /* first backtrack: quadratic fit */
+ tlmbda = -lambda * slp / ((fpls - f - slp) * LM_CNST(2.));
+ firstback = 0;
+ }
+ else { /* all subsequent backtracks: cubic fit */
+ t1 = fpls - f - lambda * slp;
+ t2 = pfpls - f - plmbda * slp;
+ t3 = LM_CNST(1.) / (lambda - plmbda);
+ a3 = LM_CNST(3.) * t3 * (t1 / (lambda * lambda)
+ - t2 / (plmbda * plmbda));
+ b = t3 * (t2 * lambda / (plmbda * plmbda)
+ - t1 * plmbda / (lambda * lambda));
+ disc = b * b - a3 * slp;
+ if (disc > b * b)
+ /* only one positive critical point, must be minimum */
+ tlmbda = (-b + ((a3 < 0)? -(LM_REAL)sqrt(disc): (LM_REAL)sqrt(disc))) /a3;
+ else
+ /* both critical points positive, first is minimum */
+ tlmbda = (-b + ((a3 < 0)? (LM_REAL)sqrt(disc): -(LM_REAL)sqrt(disc))) /a3;
+
+ if (tlmbda > lambda * LM_CNST(.5))
+ tlmbda = lambda * LM_CNST(.5);
+ }
+ plmbda = lambda;
+ pfpls = fpls;
+ if (tlmbda < lambda * LM_CNST(.1))
+ lambda *= LM_CNST(.1);
+ else
+ lambda = tlmbda;
+ }
+ }
+ }
+ /* this point is reached when the iterations limit is exceeded */
+ *iretcd = 1; /* failed */
+ return;
+} /* LNSRCH */
+
+/* Projections to feasible set \Omega: P_{\Omega}(y) := arg min { ||x - y|| : x \in \Omega}, y \in R^m */
+
+/* project vector p to a box shaped feasible set. p is a mx1 vector.
+ * Either lb, ub can be NULL. If not NULL, they are mx1 vectors
+ */
+static void BOXPROJECT(LM_REAL *p, LM_REAL *lb, LM_REAL *ub, int m)
+{
+register int i;
+
+ if(!lb){ /* no lower bounds */
+ if(!ub) /* no upper bounds */
+ return;
+ else{ /* upper bounds only */
+ for(i=0; i<m; ++i)
+ if(p[i]>ub[i]) p[i]=ub[i];
+ }
+ }
+ else
+ if(!ub){ /* lower bounds only */
+ for(i=0; i<m; ++i)
+ if(p[i]<lb[i]) p[i]=lb[i];
+ }
+ else /* box bounds */
+ for(i=0; i<m; ++i)
+ p[i]=__MEDIAN3(lb[i], p[i], ub[i]);
+}
+
+/*
+ * This function seeks the parameter vector p that best describes the measurements
+ * vector x under box constraints.
+ * More precisely, given a vector function func : R^m --> R^n with n>=m,
+ * it finds p s.t. func(p) ~= x, i.e. the squared second order (i.e. L2) norm of
+ * e=x-func(p) is minimized under the constraints lb[i]<=p[i]<=ub[i].
+ * If no lower bound constraint applies for p[i], use -DBL_MAX/-FLT_MAX for lb[i];
+ * If no upper bound constraint applies for p[i], use DBL_MAX/FLT_MAX for ub[i].
+ *
+ * This function requires an analytic Jacobian. In case the latter is unavailable,
+ * use LEVMAR_BC_DIF() bellow
+ *
+ * Returns the number of iterations (>=0) if successful, LM_ERROR if failed
+ *
+ * For details, see C. Kanzow, N. Yamashita and M. Fukushima: "Levenberg-Marquardt
+ * methods for constrained nonlinear equations with strong local convergence properties",
+ * Journal of Computational and Applied Mathematics 172, 2004, pp. 375-397.
+ * Also, see K. Madsen, H.B. Nielsen and O. Tingleff's lecture notes on
+ * unconstrained Levenberg-Marquardt at http://www.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf
+ */
+
+int LEVMAR_BC_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata), /* function to evaluate the Jacobian \part x / \part p */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *lb, /* I: vector of lower bounds. If NULL, no lower bounds apply */
+ LM_REAL *ub, /* I: vector of upper bounds. If NULL, no upper bounds apply */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[4], /* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3]. Respectively the scale factor for initial \mu,
+ * stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2. Set to NULL for defaults to be used.
+ * Note that ||J^T e||_inf is computed on free (not equal to lb[i] or ub[i]) variables only.
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_BC_DER_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func & jacf.
+ * Set to NULL if not needed
+ */
+{
+register int i, j, k, l;
+int worksz, freework=0, issolved;
+/* temp work arrays */
+LM_REAL *e, /* nx1 */
+ *hx, /* \hat{x}_i, nx1 */
+ *jacTe, /* J^T e_i mx1 */
+ *jac, /* nxm */
+ *jacTjac, /* mxm */
+ *Dp, /* mx1 */
+ *diag_jacTjac, /* diagonal of J^T J, mx1 */
+ *pDp; /* p + Dp, mx1 */
+
+register LM_REAL mu, /* damping constant */
+ tmp; /* mainly used in matrix & vector multiplications */
+LM_REAL p_eL2, jacTe_inf, pDp_eL2; /* ||e(p)||_2, ||J^T e||_inf, ||e(p+Dp)||_2 */
+LM_REAL p_L2, Dp_L2=LM_REAL_MAX, dF, dL;
+LM_REAL tau, eps1, eps2, eps2_sq, eps3;
+LM_REAL init_p_eL2;
+int nu=2, nu2, stop=0, nfev, njev=0, nlss=0;
+const int nm=n*m;
+
+/* variables for constrained LM */
+struct FUNC_STATE fstate;
+LM_REAL alpha=LM_CNST(1e-4), beta=LM_CNST(0.9), gamma=LM_CNST(0.99995), gamma_sq=gamma*gamma, rho=LM_CNST(1e-8);
+LM_REAL t, t0;
+LM_REAL steptl=LM_CNST(1e3)*(LM_REAL)sqrt(LM_REAL_EPSILON), jacTeDp;
+LM_REAL tmin=LM_CNST(1e-12), tming=LM_CNST(1e-18); /* minimum step length for LS and PG steps */
+const LM_REAL tini=LM_CNST(1.0); /* initial step length for LS and PG steps */
+int nLMsteps=0, nLSsteps=0, nPGsteps=0, gprevtaken=0;
+int numactive;
+int (*linsolver)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)=NULL;
+
+ mu=jacTe_inf=t=0.0; tmin=tmin; /* -Wall */
+
+ if(n<m){
+ fprintf(stderr, LCAT(LEVMAR_BC_DER, "(): cannot solve a problem with fewer measurements [%d] than unknowns [%d]\n"), n, m);
+ return LM_ERROR;
+ }
+
+ if(!jacf){
+ fprintf(stderr, RCAT("No function specified for computing the Jacobian in ", LEVMAR_BC_DER)
+ RCAT("().\nIf no such function is available, use ", LEVMAR_BC_DIF) RCAT("() rather than ", LEVMAR_BC_DER) "()\n");
+ return LM_ERROR;
+ }
+
+ if(!LEVMAR_BOX_CHECK(lb, ub, m)){
+ fprintf(stderr, LCAT(LEVMAR_BC_DER, "(): at least one lower bound exceeds the upper one\n"));
+ return LM_ERROR;
+ }
+
+ if(opts){
+ tau=opts[0];
+ eps1=opts[1];
+ eps2=opts[2];
+ eps2_sq=opts[2]*opts[2];
+ eps3=opts[3];
+ }
+ else{ // use default values
+ tau=LM_CNST(LM_INIT_MU);
+ eps1=LM_CNST(LM_STOP_THRESH);
+ eps2=LM_CNST(LM_STOP_THRESH);
+ eps2_sq=LM_CNST(LM_STOP_THRESH)*LM_CNST(LM_STOP_THRESH);
+ eps3=LM_CNST(LM_STOP_THRESH);
+ }
+
+ if(!work){
+ worksz=LM_BC_DER_WORKSZ(m, n); //2*n+4*m + n*m + m*m;
+ work=(LM_REAL *)malloc(worksz*sizeof(LM_REAL)); /* allocate a big chunk in one step */
+ if(!work){
+ fprintf(stderr, LCAT(LEVMAR_BC_DER, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ freework=1;
+ }
+
+ /* set up work arrays */
+ e=work;
+ hx=e + n;
+ jacTe=hx + n;
+ jac=jacTe + m;
+ jacTjac=jac + nm;
+ Dp=jacTjac + m*m;
+ diag_jacTjac=Dp + m;
+ pDp=diag_jacTjac + m;
+
+ fstate.n=n;
+ fstate.hx=hx;
+ fstate.x=x;
+ fstate.adata=adata;
+ fstate.nfev=&nfev;
+
+ /* see if starting point is within the feasile set */
+ for(i=0; i<m; ++i)
+ pDp[i]=p[i];
+ BOXPROJECT(p, lb, ub, m); /* project to feasible set */
+ for(i=0; i<m; ++i)
+ if(pDp[i]!=p[i])
+ fprintf(stderr, RCAT("Warning: component %d of starting point not feasible in ", LEVMAR_BC_DER) "()! [%g projected to %g]\n",
+ i, pDp[i], p[i]);
+
+ /* compute e=x - f(p) and its L2 norm */
+ (*func)(p, hx, m, n, adata); nfev=1;
+ /* ### e=x-hx, p_eL2=||e|| */
+#if 1
+ p_eL2=LEVMAR_L2NRMXMY(e, x, hx, n);
+#else
+ for(i=0, p_eL2=0.0; i<n; ++i){
+ e[i]=tmp=x[i]-hx[i];
+ p_eL2+=tmp*tmp;
+ }
+#endif
+ init_p_eL2=p_eL2;
+ if(!LM_FINITE(p_eL2)) stop=7;
+
+ for(k=0; k<itmax && !stop; ++k){
+ /* Note that p and e have been updated at a previous iteration */
+
+ if(p_eL2<=eps3){ /* error is small */
+ stop=6;
+ break;
+ }
+
+ /* Compute the Jacobian J at p, J^T J, J^T e, ||J^T e||_inf and ||p||^2.
+ * Since J^T J is symmetric, its computation can be sped up by computing
+ * only its upper triangular part and copying it to the lower part
+ */
+
+ (*jacf)(p, jac, m, n, adata); ++njev;
+
+ /* J^T J, J^T e */
+ if(nm<__BLOCKSZ__SQ){ // this is a small problem
+ /* J^T*J_ij = \sum_l J^T_il * J_lj = \sum_l J_li * J_lj.
+ * Thus, the product J^T J can be computed using an outer loop for
+ * l that adds J_li*J_lj to each element ij of the result. Note that
+ * with this scheme, the accesses to J and JtJ are always along rows,
+ * therefore induces less cache misses compared to the straightforward
+ * algorithm for computing the product (i.e., l loop is innermost one).
+ * A similar scheme applies to the computation of J^T e.
+ * However, for large minimization problems (i.e., involving a large number
+ * of unknowns and measurements) for which J/J^T J rows are too large to
+ * fit in the L1 cache, even this scheme incures many cache misses. In
+ * such cases, a cache-efficient blocking scheme is preferable.
+ *
+ * Thanks to John Nitao of Lawrence Livermore Lab for pointing out this
+ * performance problem.
+ *
+ * Note that the non-blocking algorithm is faster on small
+ * problems since in this case it avoids the overheads of blocking.
+ */
+ register int l, im;
+ register LM_REAL alpha, *jaclm;
+
+ /* looping downwards saves a few computations */
+ for(i=m*m; i-->0; )
+ jacTjac[i]=0.0;
+ for(i=m; i-->0; )
+ jacTe[i]=0.0;
+
+ for(l=n; l-->0; ){
+ jaclm=jac+l*m;
+ for(i=m; i-->0; ){
+ im=i*m;
+ alpha=jaclm[i]; //jac[l*m+i];
+ for(j=i+1; j-->0; ) /* j<=i computes lower triangular part only */
+ jacTjac[im+j]+=jaclm[j]*alpha; //jac[l*m+j]
+
+ /* J^T e */
+ jacTe[i]+=alpha*e[l];
+ }
+ }
+
+ for(i=m; i-->0; ) /* copy to upper part */
+ for(j=i+1; j<m; ++j)
+ jacTjac[i*m+j]=jacTjac[j*m+i];
+ }
+ else{ // this is a large problem
+ /* Cache efficient computation of J^T J based on blocking
+ */
+ LEVMAR_TRANS_MAT_MAT_MULT(jac, jacTjac, n, m);
+
+ /* cache efficient computation of J^T e */
+ for(i=0; i<m; ++i)
+ jacTe[i]=0.0;
+
+ for(i=0; i<n; ++i){
+ register LM_REAL *jacrow;
+
+ for(l=0, jacrow=jac+i*m, tmp=e[i]; l<m; ++l)
+ jacTe[l]+=jacrow[l]*tmp;
+ }
+ }
+
+ /* Compute ||J^T e||_inf and ||p||^2. Note that ||J^T e||_inf
+ * is computed for free (i.e. inactive) variables only.
+ * At a local minimum, if p[i]==ub[i] then g[i]>0;
+ * if p[i]==lb[i] g[i]<0; otherwise g[i]=0
+ */
+ for(i=j=numactive=0, p_L2=jacTe_inf=0.0; i<m; ++i){
+ if(ub && p[i]==ub[i]){ ++numactive; if(jacTe[i]>0.0) ++j; }
+ else if(lb && p[i]==lb[i]){ ++numactive; if(jacTe[i]<0.0) ++j; }
+ else if(jacTe_inf < (tmp=FABS(jacTe[i]))) jacTe_inf=tmp;
+
+ diag_jacTjac[i]=jacTjac[i*m+i]; /* save diagonal entries so that augmentation can be later canceled */
+ p_L2+=p[i]*p[i];
+ }
+ //p_L2=sqrt(p_L2);
+
+#if 0
+if(!(k%100)){
+ printf("Current estimate: ");
+ for(i=0; i<m; ++i)
+ printf("%.9g ", p[i]);
+ printf("-- errors %.9g %0.9g, #active %d [%d]\n", jacTe_inf, p_eL2, numactive, j);
+}
+#endif
+
+ /* check for convergence */
+ if(j==numactive && (jacTe_inf <= eps1)){
+ Dp_L2=0.0; /* no increment for p in this case */
+ stop=1;
+ break;
+ }
+
+ /* compute initial damping factor */
+ if(k==0){
+ if(!lb && !ub){ /* no bounds */
+ for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
+ if(diag_jacTjac[i]>tmp) tmp=diag_jacTjac[i]; /* find max diagonal element */
+ mu=tau*tmp;
+ }
+ else
+ mu=LM_CNST(0.5)*tau*p_eL2; /* use Kanzow's starting mu */
+ }
+
+ /* determine increment using a combination of adaptive damping, line search and projected gradient search */
+ while(1){
+ /* augment normal equations */
+ for(i=0; i<m; ++i)
+ jacTjac[i*m+i]+=mu;
+
+ /* solve augmented equations */
+#ifdef HAVE_LAPACK
+ /* 6 alternatives are available: LU, Cholesky, 2 variants of QR decomposition, SVD and LDLt.
+ * Cholesky is the fastest but might be inaccurate; QR is slower but more accurate;
+ * SVD is the slowest but most accurate; LU offers a tradeoff between accuracy and speed
+ */
+
+ issolved=AX_EQ_B_BK(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_BK;
+ //issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
+ //issolved=AX_EQ_B_CHOL(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_CHOL;
+ //issolved=AX_EQ_B_QR(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_QR;
+ //issolved=AX_EQ_B_QRLS(jacTjac, jacTe, Dp, m, m); ++nlss; linsolver=(int (*)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m))AX_EQ_B_QRLS;
+ //issolved=AX_EQ_B_SVD(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_SVD;
+
+#else
+ /* use the LU included with levmar */
+ issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
+#endif /* HAVE_LAPACK */
+
+ if(issolved){
+ for(i=0; i<m; ++i)
+ pDp[i]=p[i] + Dp[i];
+
+ /* compute p's new estimate and ||Dp||^2 */
+ BOXPROJECT(pDp, lb, ub, m); /* project to feasible set */
+ for(i=0, Dp_L2=0.0; i<m; ++i){
+ Dp[i]=tmp=pDp[i]-p[i];
+ Dp_L2+=tmp*tmp;
+ }
+ //Dp_L2=sqrt(Dp_L2);
+
+ if(Dp_L2<=eps2_sq*p_L2){ /* relative change in p is small, stop */
+ stop=2;
+ break;
+ }
+
+ if(Dp_L2>=(p_L2+eps2)/(LM_CNST(EPSILON)*LM_CNST(EPSILON))){ /* almost singular */
+ stop=4;
+ break;
+ }
+
+ (*func)(pDp, hx, m, n, adata); ++nfev; /* evaluate function at p + Dp */
+ /* ### hx=x-hx, pDp_eL2=||hx|| */
+#if 1
+ pDp_eL2=LEVMAR_L2NRMXMY(hx, x, hx, n);
+#else
+ for(i=0, pDp_eL2=0.0; i<n; ++i){ /* compute ||e(pDp)||_2 */
+ hx[i]=tmp=x[i]-hx[i];
+ pDp_eL2+=tmp*tmp;
+ }
+#endif
+ if(!LM_FINITE(pDp_eL2)){
+ stop=7;
+ break;
+ }
+
+ if(pDp_eL2<=gamma_sq*p_eL2){
+ for(i=0, dL=0.0; i<m; ++i)
+ dL+=Dp[i]*(mu*Dp[i]+jacTe[i]);
+
+#if 1
+ if(dL>0.0){
+ dF=p_eL2-pDp_eL2;
+ tmp=(LM_CNST(2.0)*dF/dL-LM_CNST(1.0));
+ tmp=LM_CNST(1.0)-tmp*tmp*tmp;
+ mu=mu*( (tmp>=LM_CNST(ONE_THIRD))? tmp : LM_CNST(ONE_THIRD) );
+ }
+ else
+ mu=(mu>=pDp_eL2)? pDp_eL2 : mu; /* pDp_eL2 is the new pDp_eL2 */
+#else
+
+ mu=(mu>=pDp_eL2)? pDp_eL2 : mu; /* pDp_eL2 is the new pDp_eL2 */
+#endif
+
+ nu=2;
+
+ for(i=0 ; i<m; ++i) /* update p's estimate */
+ p[i]=pDp[i];
+
+ for(i=0; i<n; ++i) /* update e and ||e||_2 */
+ e[i]=hx[i];
+ p_eL2=pDp_eL2;
+ ++nLMsteps;
+ gprevtaken=0;
+ break;
+ }
+ }
+ else{
+
+ /* the augmented linear system could not be solved, increase mu */
+
+ mu*=nu;
+ nu2=nu<<1; // 2*nu;
+ if(nu2<=nu){ /* nu has wrapped around (overflown). Thanks to Frank Jordan for spotting this case */
+ stop=5;
+ break;
+ }
+ nu=nu2;
+
+ for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
+ jacTjac[i*m+i]=diag_jacTjac[i];
+
+ continue; /* solve again with increased nu */
+ }
+
+ /* if this point is reached, the LM step did not reduce the error;
+ * see if it is a descent direction
+ */
+
+ /* negate jacTe (i.e. g) & compute g^T * Dp */
+ for(i=0, jacTeDp=0.0; i<m; ++i){
+ jacTe[i]=-jacTe[i];
+ jacTeDp+=jacTe[i]*Dp[i];
+ }
+
+ if(jacTeDp<=-rho*pow(Dp_L2, _POW_/LM_CNST(2.0))){
+ /* Dp is a descent direction; do a line search along it */
+ int mxtake, iretcd;
+ LM_REAL stepmx;
+
+ tmp=(LM_REAL)sqrt(p_L2); stepmx=LM_CNST(1e3)*( (tmp>=LM_CNST(1.0))? tmp : LM_CNST(1.0) );
+
+#if 1
+ /* use Schnabel's backtracking line search; it requires fewer "func" evaluations */
+ LNSRCH(m, p, p_eL2, jacTe, Dp, alpha, pDp, &pDp_eL2, func, fstate,
+ &mxtake, &iretcd, stepmx, steptl, NULL); /* NOTE: LNSRCH() updates hx */
+ if(iretcd!=0) goto gradproj; /* rather inelegant but effective way to handle LNSRCH() failures... */
+#else
+ /* use the simpler (but slower!) line search described by Kanzow et al */
+ for(t=tini; t>tmin; t*=beta){
+ for(i=0; i<m; ++i){
+ pDp[i]=p[i] + t*Dp[i];
+ //pDp[i]=__MEDIAN3(lb[i], pDp[i], ub[i]); /* project to feasible set */
+ }
+
+ (*func)(pDp, hx, m, n, adata); ++nfev; /* evaluate function at p + t*Dp */
+ for(i=0, pDp_eL2=0.0; i<n; ++i){ /* compute ||e(pDp)||_2 */
+ hx[i]=tmp=x[i]-hx[i];
+ pDp_eL2+=tmp*tmp;
+ }
+ if(!LM_FINITE(pDp_eL2)) goto gradproj; /* treat as line search failure */
+
+ //if(LM_CNST(0.5)*pDp_eL2<=LM_CNST(0.5)*p_eL2 + t*alpha*jacTeDp) break;
+ if(pDp_eL2<=p_eL2 + LM_CNST(2.0)*t*alpha*jacTeDp) break;
+ }
+#endif
+ ++nLSsteps;
+ gprevtaken=0;
+
+ /* NOTE: new estimate for p is in pDp, associated error in hx and its norm in pDp_eL2.
+ * These values are used below to update their corresponding variables
+ */
+ }
+ else{
+gradproj: /* Note that this point can also be reached via a goto when LNSRCH() fails */
+
+ /* jacTe is a descent direction; make a projected gradient step */
+
+ /* if the previous step was along the gradient descent, try to use the t employed in that step */
+ /* compute ||g|| */
+ for(i=0, tmp=0.0; i<m; ++i)
+ tmp+=jacTe[i]*jacTe[i];
+ tmp=(LM_REAL)sqrt(tmp);
+ tmp=LM_CNST(100.0)/(LM_CNST(1.0)+tmp);
+ t0=(tmp<=tini)? tmp : tini; /* guard against poor scaling & large steps; see (3.50) in C.T. Kelley's book */
+
+ for(t=(gprevtaken)? t : t0; t>tming; t*=beta){
+ for(i=0; i<m; ++i)
+ pDp[i]=p[i] - t*jacTe[i];
+ BOXPROJECT(pDp, lb, ub, m); /* project to feasible set */
+ for(i=0; i<m; ++i)
+ Dp[i]=pDp[i]-p[i];
+
+ (*func)(pDp, hx, m, n, adata); ++nfev; /* evaluate function at p - t*g */
+ /* compute ||e(pDp)||_2 */
+ /* ### hx=x-hx, pDp_eL2=||hx|| */
+#if 1
+ pDp_eL2=LEVMAR_L2NRMXMY(hx, x, hx, n);
+#else
+ for(i=0, pDp_eL2=0.0; i<n; ++i){
+ hx[i]=tmp=x[i]-hx[i];
+ pDp_eL2+=tmp*tmp;
+ }
+#endif
+ if(!LM_FINITE(pDp_eL2)){
+ stop=7;
+ goto breaknested;
+ }
+
+ for(i=0, tmp=0.0; i<m; ++i) /* compute ||g^T * Dp|| */
+ tmp+=jacTe[i]*Dp[i];
+
+ if(gprevtaken && pDp_eL2<=p_eL2 + LM_CNST(2.0)*LM_CNST(0.99999)*tmp){ /* starting t too small */
+ t=t0;
+ gprevtaken=0;
+ continue;
+ }
+ //if(LM_CNST(0.5)*pDp_eL2<=LM_CNST(0.5)*p_eL2 + alpha*tmp) break;
+ if(pDp_eL2<=p_eL2 + LM_CNST(2.0)*alpha*tmp) break;
+ }
+
+ ++nPGsteps;
+ gprevtaken=1;
+ /* NOTE: new estimate for p is in pDp, associated error in hx and its norm in pDp_eL2 */
+ }
+
+ /* update using computed values */
+
+ for(i=0, Dp_L2=0.0; i<m; ++i){
+ tmp=pDp[i]-p[i];
+ Dp_L2+=tmp*tmp;
+ }
+ //Dp_L2=sqrt(Dp_L2);
+
+ if(Dp_L2<=eps2_sq*p_L2){ /* relative change in p is small, stop */
+ stop=2;
+ break;
+ }
+
+ for(i=0 ; i<m; ++i) /* update p's estimate */
+ p[i]=pDp[i];
+
+ for(i=0; i<n; ++i) /* update e and ||e||_2 */
+ e[i]=hx[i];
+ p_eL2=pDp_eL2;
+ break;
+ } /* inner loop */
+ }
+
+breaknested: /* NOTE: this point is also reached via an explicit goto! */
+
+ if(k>=itmax) stop=3;
+
+ for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
+ jacTjac[i*m+i]=diag_jacTjac[i];
+
+ if(info){
+ info[0]=init_p_eL2;
+ info[1]=p_eL2;
+ info[2]=jacTe_inf;
+ info[3]=Dp_L2;
+ for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
+ if(tmp<jacTjac[i*m+i]) tmp=jacTjac[i*m+i];
+ info[4]=mu/tmp;
+ info[5]=(LM_REAL)k;
+ info[6]=(LM_REAL)stop;
+ info[7]=(LM_REAL)nfev;
+ info[8]=(LM_REAL)njev;
+ info[9]=(LM_REAL)nlss;
+ }
+
+ /* covariance matrix */
+ if(covar){
+ LEVMAR_COVAR(jacTjac, covar, p_eL2, m, n);
+ }
+
+ if(freework) free(work);
+
+#ifdef LINSOLVERS_RETAIN_MEMORY
+ if(linsolver) (*linsolver)(NULL, NULL, NULL, 0);
+#endif
+
+#if 0
+printf("%d LM steps, %d line search, %d projected gradient\n", nLMsteps, nLSsteps, nPGsteps);
+#endif
+
+ return (stop!=4 && stop!=7)? k : LM_ERROR;
+}
+
+/* following struct & LMBC_DIF_XXX functions won't be necessary if a true secant
+ * version of LEVMAR_BC_DIF() is implemented...
+ */
+struct LMBC_DIF_DATA{
+ int ffdif; // nonzero if forward differencing is used
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata);
+ LM_REAL *hx, *hxx;
+ void *adata;
+ LM_REAL delta;
+};
+
+static void LMBC_DIF_FUNC(LM_REAL *p, LM_REAL *hx, int m, int n, void *data)
+{
+struct LMBC_DIF_DATA *dta=(struct LMBC_DIF_DATA *)data;
+
+ /* call user-supplied function passing it the user-supplied data */
+ (*(dta->func))(p, hx, m, n, dta->adata);
+}
+
+static void LMBC_DIF_JACF(LM_REAL *p, LM_REAL *jac, int m, int n, void *data)
+{
+struct LMBC_DIF_DATA *dta=(struct LMBC_DIF_DATA *)data;
+
+ if(dta->ffdif){
+ /* evaluate user-supplied function at p */
+ (*(dta->func))(p, dta->hx, m, n, dta->adata);
+ LEVMAR_FDIF_FORW_JAC_APPROX(dta->func, p, dta->hx, dta->hxx, dta->delta, jac, m, n, dta->adata);
+ }
+ else
+ LEVMAR_FDIF_CENT_JAC_APPROX(dta->func, p, dta->hx, dta->hxx, dta->delta, jac, m, n, dta->adata);
+}
+
+
+/* No Jacobian version of the LEVMAR_BC_DER() function above: the Jacobian is approximated with
+ * the aid of finite differences (forward or central, see the comment for the opts argument)
+ * Ideally, this function should be implemented with a secant approach. Currently, it just calls
+ * LEVMAR_BC_DER()
+ */
+int LEVMAR_BC_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *lb, /* I: vector of lower bounds. If NULL, no lower bounds apply */
+ LM_REAL *ub, /* I: vector of upper bounds. If NULL, no upper bounds apply */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[5], /* I: opts[0-4] = minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \delta]. Respectively the
+ * scale factor for initial \mu, stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2 and
+ * the step used in difference approximation to the Jacobian. Set to NULL for defaults to be used.
+ * If \delta<0, the Jacobian is approximated with central differences which are more accurate
+ * (but slower!) compared to the forward differences employed by default.
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_BC_DIF_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func.
+ * Set to NULL if not needed
+ */
+{
+struct LMBC_DIF_DATA data;
+int ret;
+
+ //fprintf(stderr, RCAT("\nWarning: current implementation of ", LEVMAR_BC_DIF) "() does not use a secant approach!\n\n");
+
+ data.ffdif=!opts || opts[4]>=0.0;
+
+ data.func=func;
+ data.hx=(LM_REAL *)malloc(2*n*sizeof(LM_REAL)); /* allocate a big chunk in one step */
+ if(!data.hx){
+ fprintf(stderr, LCAT(LEVMAR_BC_DIF, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ data.hxx=data.hx+n;
+ data.adata=adata;
+ data.delta=(opts)? FABS(opts[4]) : (LM_REAL)LM_DIFF_DELTA;
+
+ ret=LEVMAR_BC_DER(LMBC_DIF_FUNC, LMBC_DIF_JACF, p, x, m, n, lb, ub, itmax, opts, info, work, covar, (void *)&data);
+
+ if(info){ /* correct the number of function calls */
+ if(data.ffdif)
+ info[7]+=info[8]*(m+1); /* each Jacobian evaluation costs m+1 function calls */
+ else
+ info[7]+=info[8]*(2*m); /* each Jacobian evaluation costs 2*m function calls */
+ }
+
+ free(data.hx);
+
+ return ret;
+}
+
+/* undefine everything. THIS MUST REMAIN AT THE END OF THE FILE */
+#undef FUNC_STATE
+#undef LNSRCH
+#undef BOXPROJECT
+#undef LEVMAR_BOX_CHECK
+#undef LEVMAR_BC_DER
+#undef LMBC_DIF_DATA
+#undef LMBC_DIF_FUNC
+#undef LMBC_DIF_JACF
+#undef LEVMAR_BC_DIF
+#undef LEVMAR_FDIF_FORW_JAC_APPROX
+#undef LEVMAR_FDIF_CENT_JAC_APPROX
+#undef LEVMAR_COVAR
+#undef LEVMAR_TRANS_MAT_MAT_MULT
+#undef LEVMAR_L2NRMXMY
+#undef AX_EQ_B_LU
+#undef AX_EQ_B_CHOL
+#undef AX_EQ_B_QR
+#undef AX_EQ_B_QRLS
+#undef AX_EQ_B_SVD
+#undef AX_EQ_B_BK
diff --git a/sci-libs/levmar/levmar-2.5/lmblec.c b/sci-libs/levmar/levmar-2.5/lmblec.c
new file mode 100644
index 000000000..f64c8f2ea
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmblec.c
@@ -0,0 +1,87 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-06 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/********************************************************************************
+ * combined box and linear equation constraints Levenberg-Marquardt nonlinear
+ * minimization. The same core code is used with appropriate #defines to derive
+ * single and double precision versions, see also lmblec_core.c
+ ********************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <float.h>
+
+#include "levmar.h"
+#include "misc.h"
+
+#ifndef HAVE_LAPACK
+
+#ifdef _MSC_VER
+#pragma message("Combined box and linearly constrained optimization requires LAPACK and was not compiled!")
+#else
+#warning Combined box and linearly constrained optimization requires LAPACK and was not compiled!
+#endif // _MSC_VER
+
+#else // LAPACK present
+
+#if !defined(LM_DBL_PREC) && !defined(LM_SNGL_PREC)
+#error At least one of LM_DBL_PREC, LM_SNGL_PREC should be defined!
+#endif
+
+
+#ifdef LM_SNGL_PREC
+/* single precision (float) definitions */
+#define LM_REAL float
+#define LM_PREFIX s
+
+#define LM_REAL_MAX FLT_MAX
+#define LM_REAL_MIN -FLT_MAX
+#define __SUBCNST(x) x##F
+#define LM_CNST(x) __SUBCNST(x) // force substitution
+
+#include "lmblec_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_MIN
+#undef __SUBCNST
+#undef LM_CNST
+#endif /* LM_SNGL_PREC */
+
+#ifdef LM_DBL_PREC
+/* double precision definitions */
+#define LM_REAL double
+#define LM_PREFIX d
+
+#define LM_REAL_MAX DBL_MAX
+#define LM_REAL_MIN -DBL_MAX
+#define LM_CNST(x) (x)
+
+#include "lmblec_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_MIN
+#undef LM_CNST
+#endif /* LM_DBL_PREC */
+
+#endif /* HAVE_LAPACK */
diff --git a/sci-libs/levmar/levmar-2.5/lmblec.o b/sci-libs/levmar/levmar-2.5/lmblec.o
new file mode 100644
index 000000000..ffc0fc149
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmblec.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/lmblec_core.c b/sci-libs/levmar/levmar-2.5/lmblec_core.c
new file mode 100644
index 000000000..3d3476c3a
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmblec_core.c
@@ -0,0 +1,413 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-06 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/*******************************************************************************
+ * This file implements combined box and linear equation constraints.
+ *
+ * Note that the algorithm implementing linearly constrained minimization does
+ * so by a change in parameters that transforms the original program into an
+ * unconstrained one. To employ the same idea for implementing box & linear
+ * constraints would require the transformation of box constraints on the
+ * original parameters to box constraints for the new parameter set. This
+ * being impossible, a different approach is used here for finding the minimum.
+ * The trick is to remove the box constraints by augmenting the function to
+ * be fitted with penalty terms and then solve the resulting problem (which
+ * involves linear constrains only) with the functions in lmlec.c
+ *
+ * More specifically, for the constraint a<=x[i]<=b to hold, the term C[i]=
+ * (2*x[i]-(a+b))/(b-a) should be within [-1, 1]. This is enforced by adding
+ * the penalty term w[i]*max((C[i])^2-1, 0) to the objective function, where
+ * w[i] is a large weight. In the case of constraints of the form a<=x[i],
+ * the term C[i]=a-x[i] has to be non positive, thus the penalty term is
+ * w[i]*max(C[i], 0). If x[i]<=b, C[i]=x[i]-b has to be non negative and
+ * the penalty is w[i]*max(C[i], 0). The derivatives needed for the Jacobian
+ * are as follows:
+ * For the constraint a<=x[i]<=b: 4*(2*x[i]-(a+b))/(b-a)^2 if x[i] not in [a, b],
+ * 0 otherwise
+ * For the constraint a<=x[i]: -1 if x[i]<=a, 0 otherwise
+ * For the constraint x[i]<=b: 1 if b<=x[i], 0 otherwise
+ *
+ * Note that for the above to work, the weights w[i] should be large enough;
+ * depending on your minimization problem, the default values might need some
+ * tweaking (see arg "wghts" below).
+ *******************************************************************************/
+
+#ifndef LM_REAL // not included by lmblec.c
+#error This file should not be compiled directly!
+#endif
+
+
+#define __MAX__(x, y) (((x)>=(y))? (x) : (y))
+#define __BC_WEIGHT__ LM_CNST(1E+04)
+
+#define __BC_INTERVAL__ 0
+#define __BC_LOW__ 1
+#define __BC_HIGH__ 2
+
+/* precision-specific definitions */
+#define LEVMAR_BOX_CHECK LM_ADD_PREFIX(levmar_box_check)
+#define LMBLEC_DATA LM_ADD_PREFIX(lmblec_data)
+#define LMBLEC_FUNC LM_ADD_PREFIX(lmblec_func)
+#define LMBLEC_JACF LM_ADD_PREFIX(lmblec_jacf)
+#define LEVMAR_LEC_DER LM_ADD_PREFIX(levmar_lec_der)
+#define LEVMAR_LEC_DIF LM_ADD_PREFIX(levmar_lec_dif)
+#define LEVMAR_BLEC_DER LM_ADD_PREFIX(levmar_blec_der)
+#define LEVMAR_BLEC_DIF LM_ADD_PREFIX(levmar_blec_dif)
+#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
+
+struct LMBLEC_DATA{
+ LM_REAL *x, *lb, *ub, *w;
+ int *bctype;
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata);
+ void (*jacf)(LM_REAL *p, LM_REAL *jac, int m, int n, void *adata);
+ void *adata;
+};
+
+/* augmented measurements */
+static void LMBLEC_FUNC(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata)
+{
+struct LMBLEC_DATA *data=(struct LMBLEC_DATA *)adata;
+int nn;
+register int i, j, *typ;
+register LM_REAL *lb, *ub, *w, tmp;
+
+ nn=n-m;
+ lb=data->lb;
+ ub=data->ub;
+ w=data->w;
+ typ=data->bctype;
+ (*(data->func))(p, hx, m, nn, data->adata);
+
+ for(i=nn, j=0; i<n; ++i, ++j){
+ switch(typ[j]){
+ case __BC_INTERVAL__:
+ tmp=(LM_CNST(2.0)*p[j]-(lb[j]+ub[j]))/(ub[j]-lb[j]);
+ hx[i]=w[j]*__MAX__(tmp*tmp-LM_CNST(1.0), LM_CNST(0.0));
+ break;
+
+ case __BC_LOW__:
+ hx[i]=w[j]*__MAX__(lb[j]-p[j], LM_CNST(0.0));
+ break;
+
+ case __BC_HIGH__:
+ hx[i]=w[j]*__MAX__(p[j]-ub[j], LM_CNST(0.0));
+ break;
+ }
+ }
+}
+
+/* augmented Jacobian */
+static void LMBLEC_JACF(LM_REAL *p, LM_REAL *jac, int m, int n, void *adata)
+{
+struct LMBLEC_DATA *data=(struct LMBLEC_DATA *)adata;
+int nn, *typ;
+register int i, j;
+register LM_REAL *lb, *ub, *w, tmp;
+
+ nn=n-m;
+ lb=data->lb;
+ ub=data->ub;
+ w=data->w;
+ typ=data->bctype;
+ (*(data->jacf))(p, jac, m, nn, data->adata);
+
+ /* clear all extra rows */
+ for(i=nn*m; i<n*m; ++i)
+ jac[i]=0.0;
+
+ for(i=nn, j=0; i<n; ++i, ++j){
+ switch(typ[j]){
+ case __BC_INTERVAL__:
+ if(lb[j]<=p[j] && p[j]<=ub[j])
+ continue; // corresp. jac element already 0
+
+ /* out of interval */
+ tmp=ub[j]-lb[j];
+ tmp=LM_CNST(4.0)*(LM_CNST(2.0)*p[j]-(lb[j]+ub[j]))/(tmp*tmp);
+ jac[i*m+j]=w[j]*tmp;
+ break;
+
+ case __BC_LOW__: // (lb[j]<=p[j])? 0.0 : -1.0;
+ if(lb[j]<=p[j])
+ continue; // corresp. jac element already 0
+
+ /* smaller than lower bound */
+ jac[i*m+j]=-w[j];
+ break;
+
+ case __BC_HIGH__: // (p[j]<=ub[j])? 0.0 : 1.0;
+ if(p[j]<=ub[j])
+ continue; // corresp. jac element already 0
+
+ /* greater than upper bound */
+ jac[i*m+j]=w[j];
+ break;
+ }
+ }
+}
+
+/*
+ * This function seeks the parameter vector p that best describes the measurements
+ * vector x under box & linear constraints.
+ * More precisely, given a vector function func : R^m --> R^n with n>=m,
+ * it finds p s.t. func(p) ~= x, i.e. the squared second order (i.e. L2) norm of
+ * e=x-func(p) is minimized under the constraints lb[i]<=p[i]<=ub[i] and A p=b;
+ * A is kxm, b kx1. Note that this function DOES NOT check the satisfiability of
+ * the specified box and linear equation constraints.
+ * If no lower bound constraint applies for p[i], use -DBL_MAX/-FLT_MAX for lb[i];
+ * If no upper bound constraint applies for p[i], use DBL_MAX/FLT_MAX for ub[i].
+ *
+ * This function requires an analytic Jacobian. In case the latter is unavailable,
+ * use LEVMAR_BLEC_DIF() bellow
+ *
+ * Returns the number of iterations (>=0) if successful, LM_ERROR if failed
+ *
+ * For more details on the algorithm implemented by this function, please refer to
+ * the comments in the top of this file.
+ *
+ */
+int LEVMAR_BLEC_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata), /* function to evaluate the Jacobian \part x / \part p */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *lb, /* I: vector of lower bounds. If NULL, no lower bounds apply */
+ LM_REAL *ub, /* I: vector of upper bounds. If NULL, no upper bounds apply */
+ LM_REAL *A, /* I: constraints matrix, kxm */
+ LM_REAL *b, /* I: right hand constraints vector, kx1 */
+ int k, /* I: number of constraints (i.e. A's #rows) */
+ LM_REAL *wghts, /* mx1 weights for penalty terms, defaults used if NULL */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[4], /* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3]. Respectively the scale factor for initial \mu,
+ * stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2. Set to NULL for defaults to be used
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_BLEC_DER_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func & jacf.
+ * Set to NULL if not needed
+ */
+{
+ struct LMBLEC_DATA data;
+ int ret;
+ LM_REAL locinfo[LM_INFO_SZ];
+ register int i;
+
+ if(!jacf){
+ fprintf(stderr, RCAT("No function specified for computing the Jacobian in ", LEVMAR_BLEC_DER)
+ RCAT("().\nIf no such function is available, use ", LEVMAR_BLEC_DIF) RCAT("() rather than ", LEVMAR_BLEC_DER) "()\n");
+ return LM_ERROR;
+ }
+
+ if(!lb && !ub){
+ fprintf(stderr, RCAT(LCAT(LEVMAR_BLEC_DER, "(): lower and upper bounds for box constraints cannot be both NULL, use "),
+ LEVMAR_LEC_DER) "() in this case!\n");
+ return LM_ERROR;
+ }
+
+ if(!LEVMAR_BOX_CHECK(lb, ub, m)){
+ fprintf(stderr, LCAT(LEVMAR_BLEC_DER, "(): at least one lower bound exceeds the upper one\n"));
+ return LM_ERROR;
+ }
+
+ /* measurement vector needs to be extended by m */
+ if(x){ /* nonzero x */
+ data.x=(LM_REAL *)malloc((n+m)*sizeof(LM_REAL));
+ if(!data.x){
+ fprintf(stderr, LCAT(LEVMAR_BLEC_DER, "(): memory allocation request #1 failed\n"));
+ return LM_ERROR;
+ }
+
+ for(i=0; i<n; ++i)
+ data.x[i]=x[i];
+ for(i=n; i<n+m; ++i)
+ data.x[i]=0.0;
+ }
+ else
+ data.x=NULL;
+
+ data.w=(LM_REAL *)malloc(m*sizeof(LM_REAL) + m*sizeof(int)); /* should be arranged in that order for proper doubles alignment */
+ if(!data.w){
+ fprintf(stderr, LCAT(LEVMAR_BLEC_DER, "(): memory allocation request #2 failed\n"));
+ if(data.x) free(data.x);
+ return LM_ERROR;
+ }
+ data.bctype=(int *)(data.w+m);
+
+ /* note: at this point, one of lb, ub are not NULL */
+ for(i=0; i<m; ++i){
+ data.w[i]=(!wghts)? __BC_WEIGHT__ : wghts[i];
+ if(!lb) data.bctype[i]=__BC_HIGH__;
+ else if(!ub) data.bctype[i]=__BC_LOW__;
+ else if(ub[i]!=LM_REAL_MAX && lb[i]!=LM_REAL_MIN) data.bctype[i]=__BC_INTERVAL__;
+ else if(lb[i]!=LM_REAL_MIN) data.bctype[i]=__BC_LOW__;
+ else data.bctype[i]=__BC_HIGH__;
+ }
+
+ data.lb=lb;
+ data.ub=ub;
+ data.func=func;
+ data.jacf=jacf;
+ data.adata=adata;
+
+ if(!info) info=locinfo; /* make sure that LEVMAR_LEC_DER() is called with non-null info */
+ ret=LEVMAR_LEC_DER(LMBLEC_FUNC, LMBLEC_JACF, p, data.x, m, n+m, A, b, k, itmax, opts, info, work, covar, (void *)&data);
+
+ if(data.x) free(data.x);
+ free(data.w);
+
+ return ret;
+}
+
+/* Similar to the LEVMAR_BLEC_DER() function above, except that the Jacobian is approximated
+ * with the aid of finite differences (forward or central, see the comment for the opts argument)
+ */
+int LEVMAR_BLEC_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *lb, /* I: vector of lower bounds. If NULL, no lower bounds apply */
+ LM_REAL *ub, /* I: vector of upper bounds. If NULL, no upper bounds apply */
+ LM_REAL *A, /* I: constraints matrix, kxm */
+ LM_REAL *b, /* I: right hand constraints vector, kx1 */
+ int k, /* I: number of constraints (i.e. A's #rows) */
+ LM_REAL *wghts, /* mx1 weights for penalty terms, defaults used if NULL */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[5], /* I: opts[0-3] = minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \delta]. Respectively the
+ * scale factor for initial \mu, stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2 and
+ * the step used in difference approximation to the Jacobian. Set to NULL for defaults to be used.
+ * If \delta<0, the Jacobian is approximated with central differences which are more accurate
+ * (but slower!) compared to the forward differences employed by default.
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_BLEC_DIF_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func.
+ * Set to NULL if not needed
+ */
+{
+ struct LMBLEC_DATA data;
+ int ret;
+ register int i;
+ LM_REAL locinfo[LM_INFO_SZ];
+
+ if(!lb && !ub){
+ fprintf(stderr, RCAT(LCAT(LEVMAR_BLEC_DIF, "(): lower and upper bounds for box constraints cannot be both NULL, use "),
+ LEVMAR_LEC_DIF) "() in this case!\n");
+ return LM_ERROR;
+ }
+
+ if(!LEVMAR_BOX_CHECK(lb, ub, m)){
+ fprintf(stderr, LCAT(LEVMAR_BLEC_DER, "(): at least one lower bound exceeds the upper one\n"));
+ return LM_ERROR;
+ }
+
+ /* measurement vector needs to be extended by m */
+ if(x){ /* nonzero x */
+ data.x=(LM_REAL *)malloc((n+m)*sizeof(LM_REAL));
+ if(!data.x){
+ fprintf(stderr, LCAT(LEVMAR_BLEC_DER, "(): memory allocation request #1 failed\n"));
+ return LM_ERROR;
+ }
+
+ for(i=0; i<n; ++i)
+ data.x[i]=x[i];
+ for(i=n; i<n+m; ++i)
+ data.x[i]=0.0;
+ }
+ else
+ data.x=NULL;
+
+ data.w=(LM_REAL *)malloc(m*sizeof(LM_REAL) + m*sizeof(int)); /* should be arranged in that order for proper doubles alignment */
+ if(!data.w){
+ fprintf(stderr, LCAT(LEVMAR_BLEC_DER, "(): memory allocation request #2 failed\n"));
+ if(data.x) free(data.x);
+ return LM_ERROR;
+ }
+ data.bctype=(int *)(data.w+m);
+
+ /* note: at this point, one of lb, ub are not NULL */
+ for(i=0; i<m; ++i){
+ data.w[i]=(!wghts)? __BC_WEIGHT__ : wghts[i];
+ if(!lb) data.bctype[i]=__BC_HIGH__;
+ else if(!ub) data.bctype[i]=__BC_LOW__;
+ else if(ub[i]!=LM_REAL_MAX && lb[i]!=LM_REAL_MIN) data.bctype[i]=__BC_INTERVAL__;
+ else if(lb[i]!=LM_REAL_MIN) data.bctype[i]=__BC_LOW__;
+ else data.bctype[i]=__BC_HIGH__;
+ }
+
+ data.lb=lb;
+ data.ub=ub;
+ data.func=func;
+ data.jacf=NULL;
+ data.adata=adata;
+
+ if(!info) info=locinfo; /* make sure that LEVMAR_LEC_DIF() is called with non-null info */
+ ret=LEVMAR_LEC_DIF(LMBLEC_FUNC, p, data.x, m, n+m, A, b, k, itmax, opts, info, work, covar, (void *)&data);
+
+ if(data.x) free(data.x);
+ free(data.w);
+
+ return ret;
+}
+
+/* undefine all. THIS MUST REMAIN AT THE END OF THE FILE */
+#undef LEVMAR_BOX_CHECK
+#undef LMBLEC_DATA
+#undef LMBLEC_FUNC
+#undef LMBLEC_JACF
+#undef LEVMAR_COVAR
+#undef LEVMAR_LEC_DER
+#undef LEVMAR_LEC_DIF
+#undef LEVMAR_BLEC_DER
+#undef LEVMAR_BLEC_DIF
diff --git a/sci-libs/levmar/levmar-2.5/lmbleic.c b/sci-libs/levmar/levmar-2.5/lmbleic.c
new file mode 100644
index 000000000..ca1d825eb
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmbleic.c
@@ -0,0 +1,89 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2009 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/*******************************************************************************
+ * Wrappers for linear inequality constrained Levenberg-Marquardt minimization.
+ * The same core code is used with appropriate #defines to derive single and
+ * double precision versions, see also lmbleic_core.c
+ *******************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <float.h>
+
+#include "levmar.h"
+#include "misc.h"
+
+
+#ifndef HAVE_LAPACK
+
+#ifdef _MSC_VER
+#pragma message("Linear inequalities constrained optimization requires LAPACK and was not compiled!")
+#else
+#warning Linear inequalities constrained optimization requires LAPACK and was not compiled!
+#endif // _MSC_VER
+
+#else // LAPACK present
+
+#if !defined(LM_DBL_PREC) && !defined(LM_SNGL_PREC)
+#error At least one of LM_DBL_PREC, LM_SNGL_PREC should be defined!
+#endif
+
+
+#ifdef LM_SNGL_PREC
+/* single precision (float) definitions */
+#define LM_REAL float
+#define LM_PREFIX s
+
+#define LM_REAL_MAX FLT_MAX
+#define LM_REAL_MIN -FLT_MAX
+#define __SUBCNST(x) x##F
+#define LM_CNST(x) __SUBCNST(x) // force substitution
+
+#include "lmbleic_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_MIN
+#undef __SUBCNST
+#undef LM_CNST
+#endif /* LM_SNGL_PREC */
+
+#ifdef LM_DBL_PREC
+/* double precision definitions */
+#define LM_REAL double
+#define LM_PREFIX d
+
+#define LM_REAL_MAX DBL_MAX
+#define LM_REAL_MIN -DBL_MAX
+#define LM_CNST(x) (x)
+
+#include "lmbleic_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_MAX
+#undef LM_REAL_MIN
+#undef LM_CNST
+#endif /* LM_DBL_PREC */
+
+#endif /* HAVE_LAPACK */
+
diff --git a/sci-libs/levmar/levmar-2.5/lmbleic.o b/sci-libs/levmar/levmar-2.5/lmbleic.o
new file mode 100644
index 000000000..37e47be50
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmbleic.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/lmbleic_core.c b/sci-libs/levmar/levmar-2.5/lmbleic_core.c
new file mode 100644
index 000000000..7ff1d34d0
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmbleic_core.c
@@ -0,0 +1,506 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2009 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+#ifndef LM_REAL // not included by lmbleic.c
+#error This file should not be compiled directly!
+#endif
+
+
+/* precision-specific definitions */
+#define LMBLEIC_DATA LM_ADD_PREFIX(lmbleic_data)
+#define LMBLEIC_ELIM LM_ADD_PREFIX(lmbleic_elim)
+#define LMBLEIC_FUNC LM_ADD_PREFIX(lmbleic_func)
+#define LMBLEIC_JACF LM_ADD_PREFIX(lmbleic_jacf)
+#define LEVMAR_BLEIC_DER LM_ADD_PREFIX(levmar_bleic_der)
+#define LEVMAR_BLEIC_DIF LM_ADD_PREFIX(levmar_bleic_dif)
+#define LEVMAR_BLIC_DER LM_ADD_PREFIX(levmar_blic_der)
+#define LEVMAR_BLIC_DIF LM_ADD_PREFIX(levmar_blic_dif)
+#define LEVMAR_LEIC_DER LM_ADD_PREFIX(levmar_leic_der)
+#define LEVMAR_LEIC_DIF LM_ADD_PREFIX(levmar_leic_dif)
+#define LEVMAR_LIC_DER LM_ADD_PREFIX(levmar_lic_der)
+#define LEVMAR_LIC_DIF LM_ADD_PREFIX(levmar_lic_dif)
+#define LEVMAR_BLEC_DER LM_ADD_PREFIX(levmar_blec_der)
+#define LEVMAR_BLEC_DIF LM_ADD_PREFIX(levmar_blec_dif)
+#define LEVMAR_TRANS_MAT_MAT_MULT LM_ADD_PREFIX(levmar_trans_mat_mat_mult)
+#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
+#define LEVMAR_FDIF_FORW_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_forw_jac_approx)
+
+struct LMBLEIC_DATA{
+ LM_REAL *jac;
+ int nineqcnstr; // #inequality constraints
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata);
+ void (*jacf)(LM_REAL *p, LM_REAL *jac, int m, int n, void *adata);
+ void *adata;
+};
+
+
+/* wrapper ensuring that the user-supplied function is called with the right number of variables (i.e. m) */
+static void LMBLEIC_FUNC(LM_REAL *pext, LM_REAL *hx, int mm, int n, void *adata)
+{
+struct LMBLEIC_DATA *data=(struct LMBLEIC_DATA *)adata;
+int m;
+
+ m=mm-data->nineqcnstr;
+ (*(data->func))(pext, hx, m, n, data->adata);
+}
+
+
+/* wrapper for computing the Jacobian at pext. The Jacobian is nxmm */
+static void LMBLEIC_JACF(LM_REAL *pext, LM_REAL *jacext, int mm, int n, void *adata)
+{
+struct LMBLEIC_DATA *data=(struct LMBLEIC_DATA *)adata;
+int m;
+register int i, j;
+LM_REAL *jac, *jacim, *jacextimm;
+
+ m=mm-data->nineqcnstr;
+ jac=data->jac;
+
+ (*(data->jacf))(pext, jac, m, n, data->adata);
+
+ for(i=0; i<n; ++i){
+ jacextimm=jacext+i*mm;
+ jacim=jac+i*m;
+ for(j=0; j<m; ++j)
+ jacextimm[j]=jacim[j]; //jacext[i*mm+j]=jac[i*m+j];
+
+ for(j=m; j<mm; ++j)
+ jacextimm[j]=0.0; //jacext[i*mm+j]=0.0;
+ }
+}
+
+
+/*
+ * This function is similar to LEVMAR_DER except that the minimization is
+ * performed subject to the box constraints lb[i]<=p[i]<=ub[i], the linear
+ * equation constraints A*p=b, A being k1xm, b k1x1, and the linear inequality
+ * constraints C*p>=d, C being k2xm, d k2x1.
+ *
+ * The inequalities are converted to equations by introducing surplus variables,
+ * i.e. c^T*p >= d becomes c^T*p - y = d, with y>=0. To transform all inequalities
+ * to equations, a total of k2 surplus variables are introduced; a problem with only
+ * box and linear constraints results then and is solved with LEVMAR_BLEC_DER()
+ * Note that opposite direction inequalities should be converted to the desired
+ * direction by negating, i.e. c^T*p <= d becomes -c^T*p >= -d
+ *
+ * This function requires an analytic Jacobian. In case the latter is unavailable,
+ * use LEVMAR_BLEIC_DIF() bellow
+ *
+ */
+int LEVMAR_BLEIC_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata), /* function to evaluate the Jacobian \part x / \part p */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *lb, /* I: vector of lower bounds. If NULL, no lower bounds apply */
+ LM_REAL *ub, /* I: vector of upper bounds. If NULL, no upper bounds apply */
+ LM_REAL *A, /* I: equality constraints matrix, k1xm. If NULL, no linear equation constraints apply */
+ LM_REAL *b, /* I: right hand constraints vector, k1x1 */
+ int k1, /* I: number of constraints (i.e. A's #rows) */
+ LM_REAL *C, /* I: inequality constraints matrix, k2xm */
+ LM_REAL *d, /* I: right hand constraints vector, k2x1 */
+ int k2, /* I: number of inequality constraints (i.e. C's #rows) */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[4], /* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3]. Respectively the scale factor for initial \mu,
+ * stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2. Set to NULL for defaults to be used
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_BLEIC_DER_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func & jacf.
+ * Set to NULL if not needed
+ */
+{
+ struct LMBLEIC_DATA data;
+ LM_REAL *ptr, *pext, *Aext, *bext, *covext; /* corresponding to p, A, b, covar for the full set of variables;
+ pext=[p, surplus], pext is mm, Aext is (k1+k2)xmm, bext (k1+k2), covext is mmxmm
+ */
+ LM_REAL *lbext, *ubext; // corresponding to lb, ub for the full set of variables
+ int mm, ret, k12;
+ register int i, j, ii;
+ register LM_REAL tmp;
+ LM_REAL locinfo[LM_INFO_SZ];
+
+ if(!jacf){
+ fprintf(stderr, RCAT("No function specified for computing the Jacobian in ", LEVMAR_BLEIC_DER)
+ RCAT("().\nIf no such function is available, use ", LEVMAR_BLEIC_DIF) RCAT("() rather than ", LEVMAR_BLEIC_DER) "()\n");
+ return LM_ERROR;
+ }
+
+ if(!C || !d){
+ fprintf(stderr, RCAT(LCAT(LEVMAR_BLEIC_DER, "(): missing inequality constraints, use "), LEVMAR_BLEC_DER) "() in this case!\n");
+ return LM_ERROR;
+ }
+
+ if(!A || !b) k1=0; // sanity check
+
+ mm=m+k2;
+
+ if(n<m-k1){
+ fprintf(stderr, LCAT(LEVMAR_BLEIC_DER, "(): cannot solve a problem with fewer measurements + equality constraints [%d + %d] than unknowns [%d]\n"), n, k1, m);
+ return LM_ERROR;
+ }
+
+ k12=k1+k2;
+ ptr=(LM_REAL *)malloc((3*mm + k12*mm + k12 + n*m + (covar? mm*mm : 0))*sizeof(LM_REAL));
+ if(!ptr){
+ fprintf(stderr, LCAT(LEVMAR_BLEIC_DER, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ pext=ptr;
+ lbext=pext+mm;
+ ubext=lbext+mm;
+ Aext=ubext+mm;
+ bext=Aext+k12*mm;
+ data.jac=bext+k12;
+ covext=covar? data.jac+n*m : NULL;
+ data.nineqcnstr=k2;
+ data.func=func;
+ data.jacf=jacf;
+ data.adata=adata;
+
+ /* compute y s.t. C*p - y=d, i.e. y=C*p-d.
+ * y is stored in the last k2 elements of pext
+ */
+ for(i=0; i<k2; ++i){
+ for(j=0, tmp=0.0; j<m; ++j)
+ tmp+=C[i*m+j]*p[j];
+ pext[j=i+m]=tmp-d[i];
+
+ /* surplus variables must be >=0 */
+ lbext[j]=0.0;
+ ubext[j]=LM_REAL_MAX;
+ }
+ /* set the first m elements of pext equal to p */
+ for(i=0; i<m; ++i){
+ pext[i]=p[i];
+ lbext[i]=lb? lb[i] : LM_REAL_MIN;
+ ubext[i]=ub? ub[i] : LM_REAL_MAX;
+ }
+
+ /* setup the constraints matrix */
+ /* original linear equation constraints */
+ for(i=0; i<k1; ++i){
+ for(j=0; j<m; ++j)
+ Aext[i*mm+j]=A[i*m+j];
+
+ for(j=m; j<mm; ++j)
+ Aext[i*mm+j]=0.0;
+
+ bext[i]=b[i];
+ }
+ /* linear equation constraints resulting from surplus variables */
+ for(i=0, ii=k1; i<k2; ++i, ++ii){
+ for(j=0; j<m; ++j)
+ Aext[ii*mm+j]=C[i*m+j];
+
+ for(j=m; j<mm; ++j)
+ Aext[ii*mm+j]=0.0;
+
+ Aext[ii*mm+m+i]=-1.0;
+
+ bext[ii]=d[i];
+ }
+
+ if(!info) info=locinfo; /* make sure that LEVMAR_BLEC_DER() is called with non-null info */
+ /* note that the default weights for the penalty terms are being used below */
+ ret=LEVMAR_BLEC_DER(LMBLEIC_FUNC, LMBLEIC_JACF, pext, x, mm, n, lbext, ubext, Aext, bext, k12, NULL, itmax, opts, info, work, covext, (void *)&data);
+
+ /* copy back the minimizer */
+ for(i=0; i<m; ++i)
+ p[i]=pext[i];
+
+#if 0
+printf("Surplus variables for the minimizer:\n");
+for(i=m; i<mm; ++i)
+ printf("%g ", pext[i]);
+printf("\n\n");
+#endif
+
+ if(covar){
+ for(i=0; i<m; ++i){
+ for(j=0; j<m; ++j)
+ covar[i*m+j]=covext[i*mm+j];
+ }
+ }
+
+ free(ptr);
+
+ return ret;
+}
+
+/* Similar to the LEVMAR_BLEIC_DER() function above, except that the Jacobian is approximated
+ * with the aid of finite differences (forward or central, see the comment for the opts argument)
+ */
+int LEVMAR_BLEIC_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *lb, /* I: vector of lower bounds. If NULL, no lower bounds apply */
+ LM_REAL *ub, /* I: vector of upper bounds. If NULL, no upper bounds apply */
+ LM_REAL *A, /* I: equality constraints matrix, k1xm. If NULL, no linear equation constraints apply */
+ LM_REAL *b, /* I: right hand constraints vector, k1x1 */
+ int k1, /* I: number of constraints (i.e. A's #rows) */
+ LM_REAL *C, /* I: inequality constraints matrix, k2xm */
+ LM_REAL *d, /* I: right hand constraints vector, k2x1 */
+ int k2, /* I: number of inequality constraints (i.e. C's #rows) */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[5], /* I: opts[0-3] = minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \delta]. Respectively the
+ * scale factor for initial \mu, stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2 and
+ * the step used in difference approximation to the Jacobian. Set to NULL for defaults to be used.
+ * If \delta<0, the Jacobian is approximated with central differences which are more accurate
+ * (but slower!) compared to the forward differences employed by default.
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_BLEIC_DIF_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func.
+ * Set to NULL if not needed
+ */
+{
+ struct LMBLEIC_DATA data;
+ LM_REAL *ptr, *pext, *Aext, *bext, *covext; /* corresponding to p, A, b, covar for the full set of variables;
+ pext=[p, surplus], pext is mm, Aext is (k1+k2)xmm, bext (k1+k2), covext is mmxmm
+ */
+ LM_REAL *lbext, *ubext; // corresponding to lb, ub for the full set of variables
+ int mm, ret, k12;
+ register int i, j, ii;
+ register LM_REAL tmp;
+ LM_REAL locinfo[LM_INFO_SZ];
+
+ if(!C || !d){
+ fprintf(stderr, RCAT(LCAT(LEVMAR_BLEIC_DIF, "(): missing inequality constraints, use "), LEVMAR_BLEC_DIF) "() in this case!\n");
+ return LM_ERROR;
+ }
+ if(!A || !b) k1=0; // sanity check
+
+ mm=m+k2;
+
+ if(n<m-k1){
+ fprintf(stderr, LCAT(LEVMAR_BLEIC_DIF, "(): cannot solve a problem with fewer measurements + equality constraints [%d + %d] than unknowns [%d]\n"), n, k1, m);
+ return LM_ERROR;
+ }
+
+ k12=k1+k2;
+ ptr=(LM_REAL *)malloc((3*mm + k12*mm + k12 + (covar? mm*mm : 0))*sizeof(LM_REAL));
+ if(!ptr){
+ fprintf(stderr, LCAT(LEVMAR_BLEIC_DIF, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ pext=ptr;
+ lbext=pext+mm;
+ ubext=lbext+mm;
+ Aext=ubext+mm;
+ bext=Aext+k12*mm;
+ data.jac=NULL;
+ covext=covar? bext+k12 : NULL;
+ data.nineqcnstr=k2;
+ data.func=func;
+ data.jacf=NULL;
+ data.adata=adata;
+
+ /* compute y s.t. C*p - y=d, i.e. y=C*p-d.
+ * y is stored in the last k2 elements of pext
+ */
+ for(i=0; i<k2; ++i){
+ for(j=0, tmp=0.0; j<m; ++j)
+ tmp+=C[i*m+j]*p[j];
+ pext[j=i+m]=tmp-d[i];
+
+ /* surplus variables must be >=0 */
+ lbext[j]=0.0;
+ ubext[j]=LM_REAL_MAX;
+ }
+ /* set the first m elements of pext equal to p */
+ for(i=0; i<m; ++i){
+ pext[i]=p[i];
+ lbext[i]=lb? lb[i] : LM_REAL_MIN;
+ ubext[i]=ub? ub[i] : LM_REAL_MAX;
+ }
+
+ /* setup the constraints matrix */
+ /* original linear equation constraints */
+ for(i=0; i<k1; ++i){
+ for(j=0; j<m; ++j)
+ Aext[i*mm+j]=A[i*m+j];
+
+ for(j=m; j<mm; ++j)
+ Aext[i*mm+j]=0.0;
+
+ bext[i]=b[i];
+ }
+ /* linear equation constraints resulting from surplus variables */
+ for(i=0, ii=k1; i<k2; ++i, ++ii){
+ for(j=0; j<m; ++j)
+ Aext[ii*mm+j]=C[i*m+j];
+
+ for(j=m; j<mm; ++j)
+ Aext[ii*mm+j]=0.0;
+
+ Aext[ii*mm+m+i]=-1.0;
+
+ bext[ii]=d[i];
+ }
+
+ if(!info) info=locinfo; /* make sure that LEVMAR_BLEC_DIF() is called with non-null info */
+ /* note that the default weights for the penalty terms are being used below */
+ ret=LEVMAR_BLEC_DIF(LMBLEIC_FUNC, pext, x, mm, n, lbext, ubext, Aext, bext, k12, NULL, itmax, opts, info, work, covext, (void *)&data);
+
+ /* copy back the minimizer */
+ for(i=0; i<m; ++i)
+ p[i]=pext[i];
+
+#if 0
+printf("Surplus variables for the minimizer:\n");
+for(i=m; i<mm; ++i)
+ printf("%g ", pext[i]);
+printf("\n\n");
+#endif
+
+ if(covar){
+ for(i=0; i<m; ++i){
+ for(j=0; j<m; ++j)
+ covar[i*m+j]=covext[i*mm+j];
+ }
+ }
+
+ free(ptr);
+
+ return ret;
+}
+
+
+/* convenience wrappers to LEVMAR_BLEIC_DER/LEVMAR_BLEIC_DIF */
+
+/* box & linear inequality constraints */
+int LEVMAR_BLIC_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata),
+ LM_REAL *p, LM_REAL *x, int m, int n,
+ LM_REAL *lb, LM_REAL *ub,
+ LM_REAL *C, LM_REAL *d, int k2,
+ int itmax, LM_REAL opts[4], LM_REAL info[LM_INFO_SZ], LM_REAL *work, LM_REAL *covar, void *adata)
+{
+ return LEVMAR_BLEIC_DER(func, jacf, p, x, m, n, lb, ub, NULL, NULL, 0, C, d, k2, itmax, opts, info, work, covar, adata);
+}
+
+int LEVMAR_BLIC_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ LM_REAL *p, LM_REAL *x, int m, int n,
+ LM_REAL *lb, LM_REAL *ub,
+ LM_REAL *C, LM_REAL *d, int k2,
+ int itmax, LM_REAL opts[5], LM_REAL info[LM_INFO_SZ], LM_REAL *work, LM_REAL *covar, void *adata)
+{
+ return LEVMAR_BLEIC_DIF(func, p, x, m, n, lb, ub, NULL, NULL, 0, C, d, k2, itmax, opts, info, work, covar, adata);
+}
+
+/* linear equation & inequality constraints */
+int LEVMAR_LEIC_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata),
+ LM_REAL *p, LM_REAL *x, int m, int n,
+ LM_REAL *A, LM_REAL *b, int k1,
+ LM_REAL *C, LM_REAL *d, int k2,
+ int itmax, LM_REAL opts[4], LM_REAL info[LM_INFO_SZ], LM_REAL *work, LM_REAL *covar, void *adata)
+{
+ return LEVMAR_BLEIC_DER(func, jacf, p, x, m, n, NULL, NULL, A, b, k1, C, d, k2, itmax, opts, info, work, covar, adata);
+}
+
+int LEVMAR_LEIC_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ LM_REAL *p, LM_REAL *x, int m, int n,
+ LM_REAL *A, LM_REAL *b, int k1,
+ LM_REAL *C, LM_REAL *d, int k2,
+ int itmax, LM_REAL opts[5], LM_REAL info[LM_INFO_SZ], LM_REAL *work, LM_REAL *covar, void *adata)
+{
+ return LEVMAR_BLEIC_DIF(func, p, x, m, n, NULL, NULL, A, b, k1, C, d, k2, itmax, opts, info, work, covar, adata);
+}
+
+/* linear inequality constraints */
+int LEVMAR_LIC_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata),
+ LM_REAL *p, LM_REAL *x, int m, int n,
+ LM_REAL *C, LM_REAL *d, int k2,
+ int itmax, LM_REAL opts[4], LM_REAL info[LM_INFO_SZ], LM_REAL *work, LM_REAL *covar, void *adata)
+{
+ return LEVMAR_BLEIC_DER(func, jacf, p, x, m, n, NULL, NULL, NULL, NULL, 0, C, d, k2, itmax, opts, info, work, covar, adata);
+}
+
+int LEVMAR_LIC_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ LM_REAL *p, LM_REAL *x, int m, int n,
+ LM_REAL *C, LM_REAL *d, int k2,
+ int itmax, LM_REAL opts[5], LM_REAL info[LM_INFO_SZ], LM_REAL *work, LM_REAL *covar, void *adata)
+{
+ return LEVMAR_BLEIC_DIF(func, p, x, m, n, NULL, NULL, NULL, NULL, 0, C, d, k2, itmax, opts, info, work, covar, adata);
+}
+
+/* undefine all. THIS MUST REMAIN AT THE END OF THE FILE */
+#undef LMBLEIC_DATA
+#undef LMBLEIC_ELIM
+#undef LMBLEIC_FUNC
+#undef LMBLEIC_JACF
+#undef LEVMAR_FDIF_FORW_JAC_APPROX
+#undef LEVMAR_COVAR
+#undef LEVMAR_TRANS_MAT_MAT_MULT
+#undef LEVMAR_BLEIC_DER
+#undef LEVMAR_BLEIC_DIF
+#undef LEVMAR_BLIC_DER
+#undef LEVMAR_BLIC_DIF
+#undef LEVMAR_LEIC_DER
+#undef LEVMAR_LEIC_DIF
+#undef LEVMAR_LIC_DER
+#undef LEVMAR_LIC_DIF
+#undef LEVMAR_BLEC_DER
+#undef LEVMAR_BLEC_DIF
diff --git a/sci-libs/levmar/levmar-2.5/lmdemo.c b/sci-libs/levmar/levmar-2.5/lmdemo.c
new file mode 100644
index 000000000..5ba6a9f43
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmdemo.c
@@ -0,0 +1,1230 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Demonstration driver program for the Levenberg - Marquardt minimization
+// algorithm
+// Copyright (C) 2004-05 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/********************************************************************************
+ * Levenberg-Marquardt minimization demo driver. Only the double precision versions
+ * are tested here. See the Meyer case for an example of verifying the Jacobian
+ ********************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <float.h>
+
+#include "levmar.h"
+
+#ifndef LM_DBL_PREC
+#error Demo program assumes that levmar has been compiled with double precision, see LM_DBL_PREC!
+#endif
+
+
+/* Sample functions to be minimized with LM and their Jacobians.
+ * More test functions at http://www.csit.fsu.edu/~burkardt/f_src/test_nls/test_nls.html
+ * Check also the CUTE problems collection at ftp://ftp.numerical.rl.ac.uk/pub/cute/;
+ * CUTE is searchable through http://numawww.mathematik.tu-darmstadt.de:8081/opti/select.html
+ * CUTE problems can also be solved through the AMPL web interface at http://www.ampl.com/TRYAMPL/startup.html
+ *
+ * Nonlinear optimization models in AMPL can be found at http://www.princeton.edu/~rvdb/ampl/nlmodels/
+ */
+
+#define ROSD 105.0
+
+/* Rosenbrock function, global minimum at (1, 1) */
+void ros(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ for(i=0; i<n; ++i)
+ x[i]=((1.0-p[0])*(1.0-p[0]) + ROSD*(p[1]-p[0]*p[0])*(p[1]-p[0]*p[0]));
+}
+
+void jacros(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+
+ for(i=j=0; i<n; ++i){
+ jac[j++]=(-2 + 2*p[0]-4*ROSD*(p[1]-p[0]*p[0])*p[0]);
+ jac[j++]=(2*ROSD*(p[1]-p[0]*p[0]));
+ }
+}
+
+
+#define MODROSLAM 1E02
+/* Modified Rosenbrock problem, global minimum at (1, 1) */
+void modros(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ for(i=0; i<n; i+=3){
+ x[i]=10*(p[1]-p[0]*p[0]);
+ x[i+1]=1.0-p[0];
+ x[i+2]=MODROSLAM;
+ }
+}
+
+void jacmodros(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+
+ for(i=j=0; i<n; i+=3){
+ jac[j++]=-20.0*p[0];
+ jac[j++]=10.0;
+
+ jac[j++]=-1.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ }
+}
+
+
+/* Powell's function, minimum at (0, 0) */
+void powell(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ for(i=0; i<n; i+=2){
+ x[i]=p[0];
+ x[i+1]=10.0*p[0]/(p[0]+0.1) + 2*p[1]*p[1];
+ }
+}
+
+void jacpowell(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+
+ for(i=j=0; i<n; i+=2){
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+
+ jac[j++]=1.0/((p[0]+0.1)*(p[0]+0.1));
+ jac[j++]=4.0*p[1];
+ }
+}
+
+/* Wood's function, minimum at (1, 1, 1, 1) */
+void wood(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ for(i=0; i<n; i+=6){
+ x[i]=10.0*(p[1] - p[0]*p[0]);
+ x[i+1]=1.0 - p[0];
+ x[i+2]=sqrt(90.0)*(p[3] - p[2]*p[2]);
+ x[i+3]=1.0 - p[2];
+ x[i+4]=sqrt(10.0)*(p[1]+p[3] - 2.0);
+ x[i+5]=(p[1] - p[3])/sqrt(10.0);
+ }
+}
+
+/* Meyer's (reformulated) problem, minimum at (2.48, 6.18, 3.45) */
+void meyer(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+double ui;
+
+ for(i=0; i<n; ++i){
+ ui=0.45+0.05*i;
+ x[i]=p[0]*exp(10.0*p[1]/(ui+p[2]) - 13.0);
+ }
+}
+
+void jacmeyer(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+double ui, tmp;
+
+ for(i=j=0; i<n; ++i){
+ ui=0.45+0.05*i;
+ tmp=exp(10.0*p[1]/(ui+p[2]) - 13.0);
+
+ jac[j++]=tmp;
+ jac[j++]=10.0*p[0]*tmp/(ui+p[2]);
+ jac[j++]=-10.0*p[0]*p[1]*tmp/((ui+p[2])*(ui+p[2]));
+ }
+}
+
+/* Osborne's problem, minimum at (0.3754, 1.9358, -1.4647, 0.0129, 0.0221) */
+void osborne(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+double t;
+
+ for(i=0; i<n; ++i){
+ t=10*i;
+ x[i]=p[0] + p[1]*exp(-p[3]*t) + p[2]*exp(-p[4]*t);
+ }
+}
+
+void jacosborne(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+double t, tmp1, tmp2;
+
+ for(i=j=0; i<n; ++i){
+ t=10*i;
+ tmp1=exp(-p[3]*t);
+ tmp2=exp(-p[4]*t);
+
+ jac[j++]=1.0;
+ jac[j++]=tmp1;
+ jac[j++]=tmp2;
+ jac[j++]=-p[1]*t*tmp1;
+ jac[j++]=-p[2]*t*tmp2;
+ }
+}
+
+/* helical valley function, minimum at (1.0, 0.0, 0.0) */
+#ifndef M_PI
+#define M_PI 3.14159265358979323846 /* pi */
+#endif
+
+void helval(double *p, double *x, int m, int n, void *data)
+{
+double theta;
+
+ if(p[0]<0.0)
+ theta=atan(p[1]/p[0])/(2.0*M_PI) + 0.5;
+ else if(0.0<p[0])
+ theta=atan(p[1]/p[0])/(2.0*M_PI);
+ else
+ theta=(p[1]>=0)? 0.25 : -0.25;
+
+ x[0]=10.0*(p[2] - 10.0*theta);
+ x[1]=10.0*(sqrt(p[0]*p[0] + p[1]*p[1]) - 1.0);
+ x[2]=p[2];
+}
+
+void jachelval(double *p, double *jac, int m, int n, void *data)
+{
+register int i=0;
+double tmp;
+
+ tmp=p[0]*p[0] + p[1]*p[1];
+
+ jac[i++]=50.0*p[1]/(M_PI*tmp);
+ jac[i++]=-50.0*p[0]/(M_PI*tmp);
+ jac[i++]=10.0;
+
+ jac[i++]=10.0*p[0]/sqrt(tmp);
+ jac[i++]=10.0*p[1]/sqrt(tmp);
+ jac[i++]=0.0;
+
+ jac[i++]=0.0;
+ jac[i++]=0.0;
+ jac[i++]=1.0;
+}
+
+/* Boggs - Tolle problem 3 (linearly constrained), minimum at (-0.76744, 0.25581, 0.62791, -0.11628, 0.25581)
+ * constr1: p[0] + 3*p[1] = 0;
+ * constr2: p[2] + p[3] - 2*p[4] = 0;
+ * constr3: p[1] - p[4] = 0;
+ */
+void bt3(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+double t1, t2, t3, t4;
+
+ t1=p[0]-p[1];
+ t2=p[1]+p[2]-2.0;
+ t3=p[3]-1.0;
+ t4=p[4]-1.0;
+
+ for(i=0; i<n; ++i)
+ x[i]=t1*t1 + t2*t2 + t3*t3 + t4*t4;
+}
+
+void jacbt3(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+double t1, t2, t3, t4;
+
+ t1=p[0]-p[1];
+ t2=p[1]+p[2]-2.0;
+ t3=p[3]-1.0;
+ t4=p[4]-1.0;
+
+ for(i=j=0; i<n; ++i){
+ jac[j++]=2.0*t1;
+ jac[j++]=2.0*(t2-t1);
+ jac[j++]=2.0*t2;
+ jac[j++]=2.0*t3;
+ jac[j++]=2.0*t4;
+ }
+}
+
+/* Hock - Schittkowski problem 28 (linearly constrained), minimum at (0.5, -0.5, 0.5)
+ * constr1: p[0] + 2*p[1] + 3*p[2] = 1;
+ */
+void hs28(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+double t1, t2;
+
+ t1=p[0]+p[1];
+ t2=p[1]+p[2];
+
+ for(i=0; i<n; ++i)
+ x[i]=t1*t1 + t2*t2;
+}
+
+void jachs28(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+double t1, t2;
+
+ t1=p[0]+p[1];
+ t2=p[1]+p[2];
+
+ for(i=j=0; i<n; ++i){
+ jac[j++]=2.0*t1;
+ jac[j++]=2.0*(t1+t2);
+ jac[j++]=2.0*t2;
+ }
+}
+
+/* Hock - Schittkowski problem 48 (linearly constrained), minimum at (1.0, 1.0, 1.0, 1.0, 1.0)
+ * constr1: sum {i in 0..4} p[i] = 5;
+ * constr2: p[2] - 2*(p[3]+p[4]) = -3;
+ */
+void hs48(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+double t1, t2, t3;
+
+ t1=p[0]-1.0;
+ t2=p[1]-p[2];
+ t3=p[3]-p[4];
+
+ for(i=0; i<n; ++i)
+ x[i]=t1*t1 + t2*t2 + t3*t3;
+}
+
+void jachs48(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+double t1, t2, t3;
+
+ t1=p[0]-1.0;
+ t2=p[1]-p[2];
+ t3=p[3]-p[4];
+
+ for(i=j=0; i<n; ++i){
+ jac[j++]=2.0*t1;
+ jac[j++]=2.0*t2;
+ jac[j++]=-2.0*t2;
+ jac[j++]=2.0*t3;
+ jac[j++]=-2.0*t3;
+ }
+}
+
+/* Hock - Schittkowski problem 51 (linearly constrained), minimum at (1.0, 1.0, 1.0, 1.0, 1.0)
+ * constr1: p[0] + 3*p[1] = 4;
+ * constr2: p[2] + p[3] - 2*p[4] = 0;
+ * constr3: p[1] - p[4] = 0;
+ */
+void hs51(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+double t1, t2, t3, t4;
+
+ t1=p[0]-p[1];
+ t2=p[1]+p[2]-2.0;
+ t3=p[3]-1.0;
+ t4=p[4]-1.0;
+
+ for(i=0; i<n; ++i)
+ x[i]=t1*t1 + t2*t2 + t3*t3 + t4*t4;
+}
+
+void jachs51(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+double t1, t2, t3, t4;
+
+ t1=p[0]-p[1];
+ t2=p[1]+p[2]-2.0;
+ t3=p[3]-1.0;
+ t4=p[4]-1.0;
+
+ for(i=j=0; i<n; ++i){
+ jac[j++]=2.0*t1;
+ jac[j++]=2.0*(t2-t1);
+ jac[j++]=2.0*t2;
+ jac[j++]=2.0*t3;
+ jac[j++]=2.0*t4;
+ }
+}
+
+/* Hock - Schittkowski problem 01 (box constrained), minimum at (1.0, 1.0)
+ * constr1: p[1]>=-1.5;
+ */
+void hs01(double *p, double *x, int m, int n, void *data)
+{
+double t;
+
+ t=p[0]*p[0];
+ x[0]=10.0*(p[1]-t);
+ x[1]=1.0-p[0];
+}
+
+void jachs01(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=-20.0*p[0];
+ jac[j++]=10.0;
+
+ jac[j++]=-1.0;
+ jac[j++]=0.0;
+}
+
+/* Hock - Schittkowski MODIFIED problem 21 (box constrained), minimum at (2.0, 0.0)
+ * constr1: 2 <= p[0] <=50;
+ * constr2: -50 <= p[1] <=50;
+ *
+ * Original HS21 has the additional constraint 10*p[0] - p[1] >= 10; which is inactive
+ * at the solution, so it is dropped here.
+ */
+void hs21(double *p, double *x, int m, int n, void *data)
+{
+ x[0]=p[0]/10.0;
+ x[1]=p[1];
+}
+
+void jachs21(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=0.1;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+}
+
+/* Problem hatfldb (box constrained), minimum at (0.947214, 0.8, 0.64, 0.4096)
+ * constri: p[i]>=0.0; (i=1..4)
+ * constr5: p[1]<=0.8;
+ */
+void hatfldb(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ x[0]=p[0]-1.0;
+
+ for(i=1; i<m; ++i)
+ x[i]=p[i-1]-sqrt(p[i]);
+}
+
+void jachatfldb(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=1.0;
+ jac[j++]=-0.5/sqrt(p[1]);
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=-0.5/sqrt(p[2]);
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=-0.5/sqrt(p[3]);
+}
+
+/* Problem hatfldc (box constrained), minimum at (1.0, 1.0, 1.0, 1.0)
+ * constri: p[i]>=0.0; (i=1..4)
+ * constri+4: p[i]<=10.0; (i=1..4)
+ */
+void hatfldc(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ x[0]=p[0]-1.0;
+
+ for(i=1; i<m-1; ++i)
+ x[i]=p[i-1]-sqrt(p[i]);
+
+ x[m-1]=p[m-1]-1.0;
+}
+
+void jachatfldc(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=1.0;
+ jac[j++]=-0.5/sqrt(p[1]);
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=-0.5/sqrt(p[2]);
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+}
+
+/* Hock - Schittkowski (modified #1) problem 52 (box/linearly constrained), minimum at (-0.09, 0.03, 0.25, -0.19, 0.03)
+ * constr1: p[0] + 3*p[1] = 0;
+ * constr2: p[2] + p[3] - 2*p[4] = 0;
+ * constr3: p[1] - p[4] = 0;
+ *
+ * To the above 3 constraints, we add the following 5:
+ * constr4: -0.09 <= p[0];
+ * constr5: 0.0 <= p[1] <= 0.3;
+ * constr6: p[2] <= 0.25;
+ * constr7: -0.2 <= p[3] <= 0.3;
+ * constr8: 0.0 <= p[4] <= 0.3;
+ *
+ */
+void mod1hs52(double *p, double *x, int m, int n, void *data)
+{
+ x[0]=4.0*p[0]-p[1];
+ x[1]=p[1]+p[2]-2.0;
+ x[2]=p[3]-1.0;
+ x[3]=p[4]-1.0;
+}
+
+void jacmod1hs52(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=4.0;
+ jac[j++]=-1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+}
+
+
+/* Hock - Schittkowski (modified #2) problem 52 (linear inequality constrained), minimum at (0.5, 2.0, 0.0, 1.0, 1.0)
+ * A fifth term [(p[0]-0.5)^2] is added to the objective function and
+ * the equality contraints are replaced by the following inequalities:
+ * constr1: p[0] + 3*p[1] >= -1.0;
+ * constr2: p[2] + p[3] - 2*p[4] >= -2.0;
+ * constr3: p[1] - p[4] <= 7.0;
+ *
+ *
+ */
+void mod2hs52(double *p, double *x, int m, int n, void *data)
+{
+ x[0]=4.0*p[0]-p[1];
+ x[1]=p[1]+p[2]-2.0;
+ x[2]=p[3]-1.0;
+ x[3]=p[4]-1.0;
+ x[4]=p[0]-0.5;
+}
+
+void jacmod2hs52(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=4.0;
+ jac[j++]=-1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+}
+
+/* Schittkowski (modified) problem 235 (box/linearly constrained), minimum at (-1.725, 2.9, 0.725)
+ * constr1: p[0] + p[2] = -1.0;
+ *
+ * To the above constraint, we add the following 2:
+ * constr2: p[1] - 4*p[2] = 0;
+ * constr3: 0.1 <= p[1] <= 2.9;
+ * constr4: 0.7 <= p[2];
+ *
+ */
+void mods235(double *p, double *x, int m, int n, void *data)
+{
+ x[0]=0.1*(p[0]-1.0);
+ x[1]=p[1]-p[0]*p[0];
+}
+
+void jacmods235(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=0.1;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=-2.0*p[0];
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+}
+
+/* Boggs and Tolle modified problem 7 (box/linearly constrained), minimum at (0.7, 0.49, 0.19, 1.19, -0.2)
+ * We keep the original objective function & starting point and use the following constraints:
+ *
+ * subject to cons1:
+ * x[1]+x[2] - x[3] = 1.0;
+ * subject to cons2:
+ * x[2] - x[4] + x[1] = 0.0;
+ * subject to cons3:
+ * x[5] + x[1] = 0.5;
+ * subject to cons4:
+ * x[5]>=-0.3;
+ * subject to cons5:
+ * x[1]<=0.7;
+ *
+ */
+void modbt7(double *p, double *x, int m, int n, void *data)
+{
+register int i;
+
+ for(i=0; i<n; ++i)
+ x[i]=100.0*(p[1]-p[0]*p[0])*(p[1]-p[0]*p[0]) + (p[0]-1.0)*(p[0]-1.0);
+}
+
+void jacmodbt7(double *p, double *jac, int m, int n, void *data)
+{
+register int i, j;
+
+ for(i=j=0; i<m; ++i){
+ jac[j++]=-400.0*(p[1]-p[0]*p[0])*p[0] + 2.0*p[0] - 2.0;
+ jac[j++]=200.0*(p[1]-p[0]*p[0]);
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ }
+}
+
+/* Equilibrium combustion problem, constrained nonlinear equation from the book by Floudas et al.
+ * Minimum at (0.0034, 31.3265, 0.0684, 0.8595, 0.0370)
+ * constri: p[i]>=0.0001; (i=1..5)
+ * constri+5: p[i]<=100.0; (i=1..5)
+ */
+void combust(double *p, double *x, int m, int n, void *data)
+{
+ double R, R5, R6, R7, R8, R9, R10;
+
+ R=10;
+ R5=0.193;
+ R6=4.10622*1e-4;
+ R7=5.45177*1e-4;
+ R8=4.4975*1e-7;
+ R9=3.40735*1e-5;
+ R10=9.615*1e-7;
+
+ x[0]=p[0]*p[1]+p[0]-3*p[4];
+ x[1]=2*p[0]*p[1]+p[0]+3*R10*p[1]*p[1]+p[1]*p[2]*p[2]+R7*p[1]*p[2]+R9*p[1]*p[3]+R8*p[1]-R*p[4];
+ x[2]=2*p[1]*p[2]*p[2]+R7*p[1]*p[2]+2*R5*p[2]*p[2]+R6*p[2]-8*p[4];
+ x[3]=R9*p[1]*p[3]+2*p[3]*p[3]-4*R*p[4];
+ x[4]=p[0]*p[1]+p[0]+R10*p[1]*p[1]+p[1]*p[2]*p[2]+R7*p[1]*p[2]+R9*p[1]*p[3]+R8*p[1]+R5*p[2]*p[2]+R6*p[2]+p[3]*p[3]-1.0;
+}
+
+void jaccombust(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+ double R, R5, R6, R7, R8, R9, R10;
+
+ R=10;
+ R5=0.193;
+ R6=4.10622*1e-4;
+ R7=5.45177*1e-4;
+ R8=4.4975*1e-7;
+ R9=3.40735*1e-5;
+ R10=9.615*1e-7;
+
+ for(j=0; j<m*n; ++j) jac[j]=0.0;
+
+ j=0;
+ jac[j]=p[1]+1;
+ jac[j+1]=p[0];
+ jac[j+4]=-3;
+
+ j+=m;
+ jac[j]=2*p[1]+1;
+ jac[j+1]=2*p[0]+6*R10*p[1]+p[2]*p[2]+R7*p[2]+R9*p[3]+R8;
+ jac[j+2]=2*p[1]*p[2]+R7*p[1];
+ jac[j+3]=R9*p[1];
+ jac[j+4]=-R;
+
+ j+=m;
+ jac[j+1]=2*p[2]*p[2]+R7*p[2];
+ jac[j+2]=4*p[1]*p[2]+R7*p[1]+4*R5*p[2]+R6;
+ jac[j+4]=-8;
+
+ j+=m;
+ jac[j+1]=R9*p[3];
+ jac[j+3]=R9*p[1]+4*p[3];
+ jac[j+4]=-4*R;
+
+ j+=m;
+ jac[j]=p[1]+1;
+ jac[j+1]=p[0]+2*R10*p[1]+p[2]*p[2]+R7*p[2]+R9*p[3]+R8;
+ jac[j+2]=2*p[1]*p[2]+R7*p[1]+2*R5*p[2]+R6;
+ jac[j+3]=R9*p[1]+2*p[3];
+}
+
+/* Hock - Schittkowski (modified) problem 76 (linear inequalities & equations constrained), minimum at (0.0, 0.00909091, 0.372727, 0.354545)
+ * The non-squared terms in the objective function have been removed, the rhs of constr2 has been changed to 0.4 (from 4)
+ * and constr3 has been changed to an equation.
+ *
+ * constr1: p[0] + 2*p[1] + p[2] + p[3] <= 5;
+ * constr2: 3*p[0] + p[1] + 2*p[2] - p[3] <= 0.4;
+ * constr3: p[1] + 4*p[2] = 1.5;
+ *
+ */
+void modhs76(double *p, double *x, int m, int n, void *data)
+{
+ x[0]=p[0];
+ x[1]=sqrt(0.5)*p[1];
+ x[2]=p[2];
+ x[3]=sqrt(0.5)*p[3];
+}
+
+void jacmodhs76(double *p, double *jac, int m, int n, void *data)
+{
+register int j=0;
+
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=sqrt(0.5);
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=1.0;
+ jac[j++]=0.0;
+
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=0.0;
+ jac[j++]=sqrt(0.5);
+}
+
+
+
+int main()
+{
+register int i, j;
+int problem, ret;
+double p[5], // 5 is max(2, 3, 5)
+ x[16]; // 16 is max(2, 3, 5, 6, 16)
+int m, n;
+double opts[LM_OPTS_SZ], info[LM_INFO_SZ];
+char *probname[]={
+ "Rosenbrock function",
+ "modified Rosenbrock problem",
+ "Powell's function",
+ "Wood's function",
+ "Meyer's (reformulated) problem",
+ "Osborne's problem",
+ "helical valley function",
+ "Boggs & Tolle's problem #3",
+ "Hock - Schittkowski problem #28",
+ "Hock - Schittkowski problem #48",
+ "Hock - Schittkowski problem #51",
+ "Hock - Schittkowski problem #01",
+ "Hock - Schittkowski modified problem #21",
+ "hatfldb problem",
+ "hatfldc problem",
+ "equilibrium combustion problem",
+ "Hock - Schittkowski modified #1 problem #52",
+ "Schittkowski modified problem #235",
+ "Boggs & Tolle modified problem #7",
+ "Hock - Schittkowski modified #2 problem #52",
+ "Hock - Schittkowski modified problem #76",
+};
+
+ opts[0]=LM_INIT_MU; opts[1]=1E-15; opts[2]=1E-15; opts[3]=1E-20;
+ opts[4]= LM_DIFF_DELTA; // relevant only if the Jacobian is approximated using finite differences; specifies forward differencing
+ //opts[4]=-LM_DIFF_DELTA; // specifies central differencing to approximate Jacobian; more accurate but more expensive to compute!
+
+ /* uncomment the appropriate line below to select a minimization problem */
+ problem=
+ //0; // Rosenbrock function
+ //1; // modified Rosenbrock problem
+ //2; // Powell's function
+ //3; // Wood's function
+ 4; // Meyer's (reformulated) problem
+ //5; // Osborne's problem
+ //6; // helical valley function
+#ifdef HAVE_LAPACK
+ //7; // Boggs & Tolle's problem 3
+ //8; // Hock - Schittkowski problem 28
+ //9; // Hock - Schittkowski problem 48
+ //10; // Hock - Schittkowski problem 51
+#else // no LAPACK
+#ifdef _MSC_VER
+#pragma message("LAPACK not available, some test problems cannot be used")
+#else
+#warning LAPACK not available, some test problems cannot be used
+#endif // _MSC_VER
+
+#endif /* HAVE_LAPACK */
+ //11; // Hock - Schittkowski problem 01
+ //12; // Hock - Schittkowski modified problem 21
+ //13; // hatfldb problem
+ //14; // hatfldc problem
+ //15; // equilibrium combustion problem
+#ifdef HAVE_LAPACK
+ //16; // Hock - Schittkowski modified #1 problem 52
+ //17; // Schittkowski modified problem 235
+ //18; // Boggs & Tolle modified problem #7
+ //19; // Hock - Schittkowski modified #2 problem 52
+ //20; // Hock - Schittkowski modified problem #76"
+#endif /* HAVE_LAPACK */
+
+ switch(problem){
+ default: fprintf(stderr, "unknown problem specified (#%d)! Note that some minimization problems require LAPACK.\n", problem);
+ exit(1);
+ break;
+
+ case 0:
+ /* Rosenbrock function */
+ m=2; n=2;
+ p[0]=-1.2; p[1]=1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+ ret=dlevmar_der(ros, jacros, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ //ret=dlevmar_dif(ros, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // no Jacobian
+ break;
+
+ case 1:
+ /* modified Rosenbrock problem */
+ m=2; n=3;
+ p[0]=-1.2; p[1]=1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+ ret=dlevmar_der(modros, jacmodros, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ //ret=dlevmar_dif(modros, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // no Jacobian
+ break;
+
+ case 2:
+ /* Powell's function */
+ m=2; n=2;
+ p[0]=3.0; p[1]=1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+ ret=dlevmar_der(powell, jacpowell, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ //ret=dlevmar_dif(powell, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // no Jacobian
+ break;
+
+ case 3:
+ /* Wood's function */
+ m=4; n=6;
+ p[0]=-3.0; p[1]=-1.0; p[2]=-3.0; p[3]=-1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+ ret=dlevmar_dif(wood, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // no Jacobian
+ break;
+
+ case 4:
+ /* Meyer's data fitting problem */
+ m=3; n=16;
+ p[0]=8.85; p[1]=4.0; p[2]=2.5;
+ x[0]=34.780; x[1]=28.610; x[2]=23.650; x[3]=19.630;
+ x[4]=16.370; x[5]=13.720; x[6]=11.540; x[7]=9.744;
+ x[8]=8.261; x[9]=7.030; x[10]=6.005; x[11]=5.147;
+ x[12]=4.427; x[13]=3.820; x[14]=3.307; x[15]=2.872;
+ //ret=dlevmar_der(meyer, jacmeyer, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+
+ { double *work, *covar;
+ work=malloc((LM_DIF_WORKSZ(m, n)+m*m)*sizeof(double));
+ if(!work){
+ fprintf(stderr, "memory allocation request failed in main()\n");
+ exit(1);
+ }
+ covar=work+LM_DIF_WORKSZ(m, n);
+
+ ret=dlevmar_dif(meyer, p, x, m, n, 1000, opts, info, work, covar, NULL); // no Jacobian, caller allocates work memory, covariance estimated
+
+ printf("Covariance of the fit:\n");
+ for(i=0; i<m; ++i){
+ for(j=0; j<m; ++j)
+ printf("%g ", covar[i*m+j]);
+ printf("\n");
+ }
+ printf("\n");
+
+ free(work);
+ }
+
+/* uncomment the following block to verify Jacobian */
+/*
+ {
+ double err[16];
+ dlevmar_chkjac(meyer, jacmeyer, p, m, n, NULL, err);
+ for(i=0; i<n; ++i) printf("gradient %d, err %g\n", i, err[i]);
+ }
+*/
+ break;
+
+ case 5:
+ /* Osborne's data fitting problem */
+ {
+ double x33[]={
+ 8.44E-1, 9.08E-1, 9.32E-1, 9.36E-1, 9.25E-1, 9.08E-1, 8.81E-1,
+ 8.5E-1, 8.18E-1, 7.84E-1, 7.51E-1, 7.18E-1, 6.85E-1, 6.58E-1,
+ 6.28E-1, 6.03E-1, 5.8E-1, 5.58E-1, 5.38E-1, 5.22E-1, 5.06E-1,
+ 4.9E-1, 4.78E-1, 4.67E-1, 4.57E-1, 4.48E-1, 4.38E-1, 4.31E-1,
+ 4.24E-1, 4.2E-1, 4.14E-1, 4.11E-1, 4.06E-1};
+
+ m=5; n=33;
+ p[0]=0.5; p[1]=1.5; p[2]=-1.0; p[3]=1.0E-2; p[4]=2.0E-2;
+
+ ret=dlevmar_der(osborne, jacosborne, p, x33, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ //ret=dlevmar_dif(osborne, p, x33, m, n, 1000, opts, info, NULL, NULL, NULL); // no Jacobian
+ }
+ break;
+
+ case 6:
+ /* helical valley function */
+ m=3; n=3;
+ p[0]=-1.0; p[1]=0.0; p[2]=0.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+ ret=dlevmar_der(helval, jachelval, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ //ret=dlevmar_dif(helval, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // no Jacobian
+ break;
+
+#ifdef HAVE_LAPACK
+ case 7:
+ /* Boggs-Tolle problem 3 */
+ m=5; n=5;
+ p[0]=2.0; p[1]=2.0; p[2]=2.0;
+ p[3]=2.0; p[4]=2.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[3*5]={1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, -2.0, 0.0, 1.0, 0.0, 0.0, -1.0},
+ b[3]={0.0, 0.0, 0.0};
+
+ ret=dlevmar_lec_der(bt3, jacbt3, p, x, m, n, A, b, 3, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, analytic Jacobian
+ //ret=dlevmar_lec_dif(bt3, p, x, m, n, A, b, 3, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, no Jacobian
+ }
+ break;
+
+ case 8:
+ /* Hock - Schittkowski problem 28 */
+ m=3; n=3;
+ p[0]=-4.0; p[1]=1.0; p[2]=1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[1*3]={1.0, 2.0, 3.0},
+ b[1]={1.0};
+
+ ret=dlevmar_lec_der(hs28, jachs28, p, x, m, n, A, b, 1, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, analytic Jacobian
+ //ret=dlevmar_lec_dif(hs28, p, x, m, n, A, b, 1, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, no Jacobian
+ }
+ break;
+
+ case 9:
+ /* Hock - Schittkowski problem 48 */
+ m=5; n=5;
+ p[0]=3.0; p[1]=5.0; p[2]=-3.0;
+ p[3]=2.0; p[4]=-2.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[2*5]={1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, -2.0, -2.0},
+ b[2]={5.0, -3.0};
+
+ ret=dlevmar_lec_der(hs48, jachs48, p, x, m, n, A, b, 2, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, analytic Jacobian
+ //ret=dlevmar_lec_dif(hs48, p, x, m, n, A, b, 2, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, no Jacobian
+ }
+ break;
+
+ case 10:
+ /* Hock - Schittkowski problem 51 */
+ m=5; n=5;
+ p[0]=2.5; p[1]=0.5; p[2]=2.0;
+ p[3]=-1.0; p[4]=0.5;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[3*5]={1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, -2.0, 0.0, 1.0, 0.0, 0.0, -1.0},
+ b[3]={4.0, 0.0, 0.0};
+
+ ret=dlevmar_lec_der(hs51, jachs51, p, x, m, n, A, b, 3, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, analytic Jacobian
+ //ret=dlevmar_lec_dif(hs51, p, x, m, n, A, b, 3, 1000, opts, info, NULL, NULL, NULL); // lin. constraints, no Jacobian
+ }
+ break;
+
+#endif /* HAVE_LAPACK */
+
+ case 11:
+ /* Hock - Schittkowski problem 01 */
+ m=2; n=2;
+ p[0]=-2.0; p[1]=1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+ //ret=dlevmar_der(hs01, jachs01, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ {
+ double lb[2], ub[2];
+
+ lb[0]=-DBL_MAX; lb[1]=-1.5;
+ ub[0]=ub[1]=DBL_MAX;
+
+ ret=dlevmar_bc_der(hs01, jachs01, p, x, m, n, lb, ub, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ }
+ break;
+
+ case 12:
+ /* Hock - Schittkowski (modified) problem 21 */
+ m=2; n=2;
+ p[0]=-1.0; p[1]=-1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+ //ret=dlevmar_der(hs21, jachs21, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ {
+ double lb[2], ub[2];
+
+ lb[0]=2.0; lb[1]=-50.0;
+ ub[0]=50.0; ub[1]=50.0;
+
+ ret=dlevmar_bc_der(hs21, jachs21, p, x, m, n, lb, ub, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ }
+ break;
+
+ case 13:
+ /* hatfldb problem */
+ m=4; n=4;
+ p[0]=p[1]=p[2]=p[3]=0.1;
+ for(i=0; i<n; i++) x[i]=0.0;
+ //ret=dlevmar_der(hatfldb, jachatfldb, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ {
+ double lb[4], ub[4];
+
+ lb[0]=lb[1]=lb[2]=lb[3]=0.0;
+
+ ub[0]=ub[2]=ub[3]=DBL_MAX;
+ ub[1]=0.8;
+
+ ret=dlevmar_bc_der(hatfldb, jachatfldb, p, x, m, n, lb, ub, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ }
+ break;
+
+ case 14:
+ /* hatfldc problem */
+ m=4; n=4;
+ p[0]=p[1]=p[2]=p[3]=0.9;
+ for(i=0; i<n; i++) x[i]=0.0;
+ //ret=dlevmar_der(hatfldc, jachatfldc, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ {
+ double lb[4], ub[4];
+
+ lb[0]=lb[1]=lb[2]=lb[3]=0.0;
+
+ ub[0]=ub[1]=ub[2]=ub[3]=10.0;
+
+ ret=dlevmar_bc_der(hatfldc, jachatfldc, p, x, m, n, lb, ub, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ }
+ break;
+
+ case 15:
+ /* equilibrium combustion problem */
+ m=5; n=5;
+ p[0]=p[1]=p[2]=p[3]=p[4]=0.0001;
+ for(i=0; i<n; i++) x[i]=0.0;
+ //ret=dlevmar_der(combust, jaccombust, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ {
+ double lb[5], ub[5];
+
+ lb[0]=lb[1]=lb[2]=lb[3]=lb[4]=0.0001;
+
+ ub[0]=ub[1]=ub[2]=ub[3]=ub[4]=100.0;
+
+ ret=dlevmar_bc_der(combust, jaccombust, p, x, m, n, lb, ub, 5000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
+ }
+ break;
+
+#ifdef HAVE_LAPACK
+ case 16:
+ /* Hock - Schittkowski modified #1 problem 52 */
+ m=5; n=4;
+ p[0]=2.0; p[1]=2.0; p[2]=2.0;
+ p[3]=2.0; p[4]=2.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[3*5]={1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, -2.0, 0.0, 1.0, 0.0, 0.0, -1.0},
+ b[3]={0.0, 0.0, 0.0};
+
+ double lb[5], ub[5];
+
+ double weights[5]={2000.0, 2000.0, 2000.0, 2000.0, 2000.0}; // penalty terms weights
+
+ lb[0]=-0.09; lb[1]=0.0; lb[2]=-DBL_MAX; lb[3]=-0.2; lb[4]=0.0;
+ ub[0]=DBL_MAX; ub[1]=0.3; ub[2]=0.25; ub[3]=0.3; ub[4]=0.3;
+
+ ret=dlevmar_blec_der(mod1hs52, jacmod1hs52, p, x, m, n, lb, ub, A, b, 3, weights, 1000, opts, info, NULL, NULL, NULL); // box & lin. constraints, analytic Jacobian
+ //ret=dlevmar_blec_dif(mod1hs52, p, x, m, n, lb, ub, A, b, 3, weights, 1000, opts, info, NULL, NULL, NULL); // box & lin. constraints, no Jacobian
+ }
+ break;
+
+ case 17:
+ /* Schittkowski modified problem 235 */
+ m=3; n=2;
+ p[0]=-2.0; p[1]=3.0; p[2]=1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[2*3]={1.0, 0.0, 1.0, 0.0, 1.0, -4.0},
+ b[2]={-1.0, 0.0};
+
+ double lb[3], ub[3];
+
+ lb[0]=-DBL_MAX; lb[1]=0.1; lb[2]=0.7;
+ ub[0]=DBL_MAX; ub[1]=2.9; ub[2]=DBL_MAX;
+
+ ret=dlevmar_blec_der(mods235, jacmods235, p, x, m, n, lb, ub, A, b, 2, NULL, 1000, opts, info, NULL, NULL, NULL); // box & lin. constraints, analytic Jacobian
+ //ret=dlevmar_blec_dif(mods235, p, x, m, n, lb, ub, A, b, 2, NULL, 1000, opts, info, NULL, NULL, NULL); // box & lin. constraints, no Jacobian
+ }
+ break;
+
+ case 18:
+ /* Boggs & Tolle modified problem 7 */
+ m=5; n=5;
+ p[0]=-2.0; p[1]=1.0; p[2]=1.0; p[3]=1.0; p[4]=1.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[3*5]={1.0, 1.0, -1.0, 0.0, 0.0, 1.0, 1.0, 0.0, -1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0},
+ b[3]={1.0, 0.0, 0.5};
+
+ double lb[5], ub[5];
+
+ lb[0]=-DBL_MAX; lb[1]=-DBL_MAX; lb[2]=-DBL_MAX; lb[3]=-DBL_MAX; lb[4]=-0.3;
+ ub[0]=0.7; ub[1]= DBL_MAX; ub[2]= DBL_MAX; ub[3]= DBL_MAX; ub[4]=DBL_MAX;
+
+ ret=dlevmar_blec_der(modbt7, jacmodbt7, p, x, m, n, lb, ub, A, b, 3, NULL, 1000, opts, info, NULL, NULL, NULL); // box & lin. constraints, analytic Jacobian
+ //ret=dlevmar_blec_dif(modbt7, p, x, m, n, lb, ub, A, b, 3, NULL, 10000, opts, info, NULL, NULL, NULL); // box & lin. constraints, no Jacobian
+ }
+ break;
+
+ case 19:
+ /* Hock - Schittkowski modified #2 problem 52 */
+ m=5; n=5;
+ p[0]=2.0; p[1]=2.0; p[2]=2.0;
+ p[3]=2.0; p[4]=2.0;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double C[3*5]={1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, -2.0, 0.0, -1.0, 0.0, 0.0, 1.0},
+ d[3]={-1.0, -2.0, -7.0};
+
+ ret=dlevmar_bleic_der(mod2hs52, jacmod2hs52, p, x, m, n, NULL, NULL, NULL, NULL, 0, C, d, 3, 1000, opts, info, NULL, NULL, NULL); // lin. ineq. constraints, analytic Jacobian
+ //ret=dlevmar_bleic_dif(mod2hs52, p, x, m, n, NULL, NULL, NULL, NULL, 0, C, d, 3, 1000, opts, info, NULL, NULL, NULL); // lin. ineq. constraints, no Jacobian
+ }
+ break;
+
+ case 20:
+ /* Hock - Schittkowski modified problem 76 */
+ m=4; n=4;
+ p[0]=0.5; p[1]=0.5; p[2]=0.5; p[3]=0.5;
+ for(i=0; i<n; i++) x[i]=0.0;
+
+ {
+ double A[1*4]={0.0, 1.0, 4.0, 0.0},
+ b[1]={1.5};
+
+ double C[2*4]={-1.0, -2.0, -1.0, -1.0, -3.0, -1.0, -2.0, 1.0},
+ d[2]={-5.0, -0.4};
+
+ double lb[4]={0.0, 0.0, 0.0, 0.0};
+
+ ret=dlevmar_bleic_der(modhs76, jacmodhs76, p, x, m, n, lb, NULL, A, b, 1, C, d, 2, 1000, opts, info, NULL, NULL, NULL); // lin. ineq. constraints, analytic Jacobian
+ //ret=dlevmar_bleic_dif(modhs76, p, x, m, n, lb, NULL, A, b, 1, C, d, 2, 1000, opts, info, NULL, NULL, NULL); // lin. ineq. constraints, no Jacobian
+ /* variations:
+ * if no lb is used, the minimizer is (-0.1135922 0.1330097 0.3417476 0.07572816)
+ * if the rhs of constr2 is 4.0, the minimizer is (0.0, 0.166667, 0.333333, 0.0)
+ */
+ }
+ break;
+
+#endif /* HAVE_LAPACK */
+ } /* switch */
+
+ printf("Results for %s:\n", probname[problem]);
+ printf("Levenberg-Marquardt returned %d in %g iter, reason %g\nSolution: ", ret, info[5], info[6]);
+ for(i=0; i<m; ++i)
+ printf("%.7g ", p[i]);
+ printf("\n\nMinimization info:\n");
+ for(i=0; i<LM_INFO_SZ; ++i)
+ printf("%g ", info[i]);
+ printf("\n");
+
+ return 0;
+}
diff --git a/sci-libs/levmar/levmar-2.5/lmdemo.o b/sci-libs/levmar/levmar-2.5/lmdemo.o
new file mode 100644
index 000000000..817e5b31b
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmdemo.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/lmdemo.vcproj b/sci-libs/levmar/levmar-2.5/lmdemo.vcproj
new file mode 100644
index 000000000..ce2b9b584
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmdemo.vcproj
@@ -0,0 +1,194 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<VisualStudioProject
+ ProjectType="Visual C++"
+ Version="8,00"
+ Name="lmdemo"
+ ProjectGUID="{4A085982-23EF-4B63-8D72-B901D0521326}"
+ Keyword="Win32Proj"
+ >
+ <Platforms>
+ <Platform
+ Name="Win32"
+ />
+ </Platforms>
+ <ToolFiles>
+ </ToolFiles>
+ <Configurations>
+ <Configuration
+ Name="Debug|Win32"
+ OutputDirectory="Debug"
+ IntermediateDirectory="Debug"
+ ConfigurationType="1"
+ >
+ <Tool
+ Name="VCPreBuildEventTool"
+ />
+ <Tool
+ Name="VCCustomBuildTool"
+ />
+ <Tool
+ Name="VCXMLDataGeneratorTool"
+ />
+ <Tool
+ Name="VCWebServiceProxyGeneratorTool"
+ />
+ <Tool
+ Name="VCMIDLTool"
+ />
+ <Tool
+ Name="VCCLCompilerTool"
+ Optimization="0"
+ AdditionalIncludeDirectories="."
+ PreprocessorDefinitions="WIN32;_DEBUG;_CONSOLE"
+ MinimalRebuild="true"
+ BasicRuntimeChecks="3"
+ RuntimeLibrary="3"
+ UsePrecompiledHeader="0"
+ WarningLevel="3"
+ Detect64BitPortabilityProblems="true"
+ DebugInformationFormat="4"
+ CompileAs="1"
+ />
+ <Tool
+ Name="VCManagedResourceCompilerTool"
+ />
+ <Tool
+ Name="VCResourceCompilerTool"
+ />
+ <Tool
+ Name="VCPreLinkEventTool"
+ />
+ <Tool
+ Name="VCLinkerTool"
+ AdditionalDependencies="levmar.lib clapack.lib blas.lib libF77.lib libI77.lib"
+ AdditionalLibraryDirectories=".\Debug;c:\RECOVER\src\lib"
+ GenerateManifest="true"
+ />
+ <Tool
+ Name="VCALinkTool"
+ />
+ <Tool
+ Name="VCManifestTool"
+ />
+ <Tool
+ Name="VCXDCMakeTool"
+ />
+ <Tool
+ Name="VCBscMakeTool"
+ />
+ <Tool
+ Name="VCFxCopTool"
+ />
+ <Tool
+ Name="VCAppVerifierTool"
+ />
+ <Tool
+ Name="VCWebDeploymentTool"
+ />
+ <Tool
+ Name="VCPostBuildEventTool"
+ />
+ </Configuration>
+ <Configuration
+ Name="Release|Win32"
+ OutputDirectory="Release"
+ IntermediateDirectory="Release"
+ ConfigurationType="1"
+ >
+ <Tool
+ Name="VCPreBuildEventTool"
+ />
+ <Tool
+ Name="VCCustomBuildTool"
+ />
+ <Tool
+ Name="VCXMLDataGeneratorTool"
+ />
+ <Tool
+ Name="VCWebServiceProxyGeneratorTool"
+ />
+ <Tool
+ Name="VCMIDLTool"
+ />
+ <Tool
+ Name="VCCLCompilerTool"
+ AdditionalIncludeDirectories="."
+ PreprocessorDefinitions="WIN32;NDEBUG;_CONSOLE"
+ RuntimeLibrary="2"
+ UsePrecompiledHeader="0"
+ WarningLevel="3"
+ Detect64BitPortabilityProblems="true"
+ DebugInformationFormat="3"
+ CompileAs="1"
+ />
+ <Tool
+ Name="VCManagedResourceCompilerTool"
+ />
+ <Tool
+ Name="VCResourceCompilerTool"
+ />
+ <Tool
+ Name="VCPreLinkEventTool"
+ />
+ <Tool
+ Name="VCLinkerTool"
+ AdditionalDependencies="levmar.lib clapack.lib blas.lib libF77.lib libI77.lib"
+ AdditionalLibraryDirectories=".\Release;c:\RECOVER\src\lib"
+ GenerateManifest="true"
+ />
+ <Tool
+ Name="VCALinkTool"
+ />
+ <Tool
+ Name="VCManifestTool"
+ />
+ <Tool
+ Name="VCXDCMakeTool"
+ />
+ <Tool
+ Name="VCBscMakeTool"
+ />
+ <Tool
+ Name="VCFxCopTool"
+ />
+ <Tool
+ Name="VCAppVerifierTool"
+ />
+ <Tool
+ Name="VCWebDeploymentTool"
+ />
+ <Tool
+ Name="VCPostBuildEventTool"
+ />
+ </Configuration>
+ </Configurations>
+ <References>
+ </References>
+ <Files>
+ <Filter
+ Name="Header Files"
+ Filter="h;hpp;hxx;hm;inl;inc;xsd"
+ >
+ <File
+ RelativePath=".\levmar.h"
+ >
+ </File>
+ </Filter>
+ <Filter
+ Name="Resource Files"
+ Filter="rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx"
+ >
+ </Filter>
+ <Filter
+ Name="Source Files"
+ Filter="cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx"
+ >
+ <File
+ RelativePath=".\lmdemo.c"
+ >
+ </File>
+ </Filter>
+ </Files>
+ <Globals>
+ </Globals>
+</VisualStudioProject>
diff --git a/sci-libs/levmar/levmar-2.5/lmlec.c b/sci-libs/levmar/levmar-2.5/lmlec.c
new file mode 100644
index 000000000..d5cea7acb
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmlec.c
@@ -0,0 +1,80 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-05 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/*******************************************************************************
+ * Wrappers for linearly constrained Levenberg-Marquardt minimization. The same
+ * core code is used with appropriate #defines to derive single and double
+ * precision versions, see also lmlec_core.c
+ *******************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include "levmar.h"
+#include "misc.h"
+
+
+#ifndef HAVE_LAPACK
+
+#ifdef _MSC_VER
+#pragma message("Linearly constrained optimization requires LAPACK and was not compiled!")
+#else
+#warning Linearly constrained optimization requires LAPACK and was not compiled!
+#endif // _MSC_VER
+
+#else // LAPACK present
+
+#if !defined(LM_DBL_PREC) && !defined(LM_SNGL_PREC)
+#error At least one of LM_DBL_PREC, LM_SNGL_PREC should be defined!
+#endif
+
+
+#ifdef LM_SNGL_PREC
+/* single precision (float) definitions */
+#define LM_REAL float
+#define LM_PREFIX s
+
+#define __SUBCNST(x) x##F
+#define LM_CNST(x) __SUBCNST(x) // force substitution
+
+#include "lmlec_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef __SUBCNST
+#undef LM_CNST
+#endif /* LM_SNGL_PREC */
+
+#ifdef LM_DBL_PREC
+/* double precision definitions */
+#define LM_REAL double
+#define LM_PREFIX d
+
+#define LM_CNST(x) (x)
+
+#include "lmlec_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_CNST
+#endif /* LM_DBL_PREC */
+
+#endif /* HAVE_LAPACK */
+
diff --git a/sci-libs/levmar/levmar-2.5/lmlec.o b/sci-libs/levmar/levmar-2.5/lmlec.o
new file mode 100644
index 000000000..fc73c0f39
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmlec.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/lmlec_core.c b/sci-libs/levmar/levmar-2.5/lmlec_core.c
new file mode 100644
index 000000000..083408895
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/lmlec_core.c
@@ -0,0 +1,657 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-05 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+#ifndef LM_REAL // not included by lmlec.c
+#error This file should not be compiled directly!
+#endif
+
+
+/* precision-specific definitions */
+#define LMLEC_DATA LM_ADD_PREFIX(lmlec_data)
+#define LMLEC_ELIM LM_ADD_PREFIX(lmlec_elim)
+#define LMLEC_FUNC LM_ADD_PREFIX(lmlec_func)
+#define LMLEC_JACF LM_ADD_PREFIX(lmlec_jacf)
+#define LEVMAR_LEC_DER LM_ADD_PREFIX(levmar_lec_der)
+#define LEVMAR_LEC_DIF LM_ADD_PREFIX(levmar_lec_dif)
+#define LEVMAR_DER LM_ADD_PREFIX(levmar_der)
+#define LEVMAR_DIF LM_ADD_PREFIX(levmar_dif)
+#define LEVMAR_TRANS_MAT_MAT_MULT LM_ADD_PREFIX(levmar_trans_mat_mat_mult)
+#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
+#define LEVMAR_FDIF_FORW_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_forw_jac_approx)
+
+#define GEQP3 LM_MK_LAPACK_NAME(geqp3)
+#define ORGQR LM_MK_LAPACK_NAME(orgqr)
+#define TRTRI LM_MK_LAPACK_NAME(trtri)
+
+struct LMLEC_DATA{
+ LM_REAL *c, *Z, *p, *jac;
+ int ncnstr;
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata);
+ void (*jacf)(LM_REAL *p, LM_REAL *jac, int m, int n, void *adata);
+ void *adata;
+};
+
+/* prototypes for LAPACK routines */
+extern int GEQP3(int *m, int *n, LM_REAL *a, int *lda, int *jpvt,
+ LM_REAL *tau, LM_REAL *work, int *lwork, int *info);
+
+extern int ORGQR(int *m, int *n, int *k, LM_REAL *a, int *lda, LM_REAL *tau,
+ LM_REAL *work, int *lwork, int *info);
+
+extern int TRTRI(char *uplo, char *diag, int *n, LM_REAL *a, int *lda, int *info);
+
+/*
+ * This function implements an elimination strategy for linearly constrained
+ * optimization problems. The strategy relies on QR decomposition to transform
+ * an optimization problem constrained by Ax=b to an equivalent, unconstrained
+ * one. Also referred to as "null space" or "reduced Hessian" method.
+ * See pp. 430-433 (chap. 15) of "Numerical Optimization" by Nocedal-Wright
+ * for details.
+ *
+ * A is mxn with m<=n and rank(A)=m
+ * Two matrices Y and Z of dimensions nxm and nx(n-m) are computed from A^T so that
+ * their columns are orthonormal and every x can be written as x=Y*b + Z*x_z=
+ * c + Z*x_z, where c=Y*b is a fixed vector of dimension n and x_z is an
+ * arbitrary vector of dimension n-m. Then, the problem of minimizing f(x)
+ * subject to Ax=b is equivalent to minimizing f(c + Z*x_z) with no constraints.
+ * The computed Y and Z are such that any solution of Ax=b can be written as
+ * x=Y*x_y + Z*x_z for some x_y, x_z. Furthermore, A*Y is nonsingular, A*Z=0
+ * and Z spans the null space of A.
+ *
+ * The function accepts A, b and computes c, Y, Z. If b or c is NULL, c is not
+ * computed. Also, Y can be NULL in which case it is not referenced.
+ * The function returns LM_ERROR in case of error, A's computed rank if successful
+ *
+ */
+static int LMLEC_ELIM(LM_REAL *A, LM_REAL *b, LM_REAL *c, LM_REAL *Y, LM_REAL *Z, int m, int n)
+{
+static LM_REAL eps=LM_CNST(-1.0);
+
+LM_REAL *buf=NULL;
+LM_REAL *a, *tau, *work, *r, aux;
+register LM_REAL tmp;
+int a_sz, jpvt_sz, tau_sz, r_sz, Y_sz, worksz;
+int info, rank, *jpvt, tot_sz, mintmn, tm, tn;
+register int i, j, k;
+
+ if(m>n){
+ fprintf(stderr, RCAT("matrix of constraints cannot have more rows than columns in", LMLEC_ELIM) "()!\n");
+ return LM_ERROR;
+ }
+
+ tm=n; tn=m; // transpose dimensions
+ mintmn=m;
+
+ /* calculate required memory size */
+ worksz=-1; // workspace query. Optimal work size is returned in aux
+ //ORGQR((int *)&tm, (int *)&tm, (int *)&mintmn, NULL, (int *)&tm, NULL, (LM_REAL *)&aux, &worksz, &info);
+ GEQP3((int *)&tm, (int *)&tn, NULL, (int *)&tm, NULL, NULL, (LM_REAL *)&aux, (int *)&worksz, &info);
+ worksz=(int)aux;
+ a_sz=tm*tm; // tm*tn is enough for xgeqp3()
+ jpvt_sz=tn;
+ tau_sz=mintmn;
+ r_sz=mintmn*mintmn; // actually smaller if a is not of full row rank
+ Y_sz=(Y)? 0 : tm*tn;
+
+ tot_sz=(a_sz + tau_sz + r_sz + worksz + Y_sz)*sizeof(LM_REAL) + jpvt_sz*sizeof(int); /* should be arranged in that order for proper doubles alignment */
+ buf=(LM_REAL *)malloc(tot_sz); /* allocate a "big" memory chunk at once */
+ if(!buf){
+ fprintf(stderr, RCAT("Memory allocation request failed in ", LMLEC_ELIM) "()\n");
+ return LM_ERROR;
+ }
+
+ a=buf;
+ tau=a+a_sz;
+ r=tau+tau_sz;
+ work=r+r_sz;
+ if(!Y){
+ Y=work+worksz;
+ jpvt=(int *)(Y+Y_sz);
+ }
+ else
+ jpvt=(int *)(work+worksz);
+
+ /* copy input array so that LAPACK won't destroy it. Note that copying is
+ * done in row-major order, which equals A^T in column-major
+ */
+ for(i=0; i<tm*tn; ++i)
+ a[i]=A[i];
+
+ /* clear jpvt */
+ for(i=0; i<jpvt_sz; ++i) jpvt[i]=0;
+
+ /* rank revealing QR decomposition of A^T*/
+ GEQP3((int *)&tm, (int *)&tn, a, (int *)&tm, jpvt, tau, work, (int *)&worksz, &info);
+ //dgeqpf_((int *)&tm, (int *)&tn, a, (int *)&tm, jpvt, tau, work, &info);
+ /* error checking */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", GEQP3) " in ", LMLEC_ELIM) "()\n", -info);
+ }
+ else if(info>0){
+ fprintf(stderr, RCAT(RCAT("unknown LAPACK error (%d) for ", GEQP3) " in ", LMLEC_ELIM) "()\n", info);
+ }
+ free(buf);
+ return LM_ERROR;
+ }
+ /* the upper triangular part of a now contains the upper triangle of the unpermuted R */
+
+ if(eps<0.0){
+ LM_REAL aux;
+
+ /* compute machine epsilon. DBL_EPSILON should do also */
+ for(eps=LM_CNST(1.0); aux=eps+LM_CNST(1.0), aux-LM_CNST(1.0)>0.0; eps*=LM_CNST(0.5))
+ ;
+ eps*=LM_CNST(2.0);
+ }
+
+ tmp=tm*LM_CNST(10.0)*eps*FABS(a[0]); // threshold. tm is max(tm, tn)
+ tmp=(tmp>LM_CNST(1E-12))? tmp : LM_CNST(1E-12); // ensure that threshold is not too small
+ /* compute A^T's numerical rank by counting the non-zeros in R's diagonal */
+ for(i=rank=0; i<mintmn; ++i)
+ if(a[i*(tm+1)]>tmp || a[i*(tm+1)]<-tmp) ++rank; /* loop across R's diagonal elements */
+ else break; /* diagonal is arranged in absolute decreasing order */
+
+ if(rank<tn){
+ fprintf(stderr, RCAT("\nConstraints matrix in ", LMLEC_ELIM) "() is not of full row rank (i.e. %d < %d)!\n"
+ "Make sure that you do not specify redundant or inconsistent constraints.\n\n", rank, tn);
+ free(buf);
+ return LM_ERROR;
+ }
+
+ /* compute the permuted inverse transpose of R */
+ /* first, copy R from the upper triangular part of a to r. R is rank x rank */
+ for(j=0; j<rank; ++j){
+ for(i=0; i<=j; ++i)
+ r[i+j*rank]=a[i+j*tm];
+ for(i=j+1; i<rank; ++i)
+ r[i+j*rank]=0.0; // lower part is zero
+ }
+
+ /* compute the inverse */
+ TRTRI("U", "N", (int *)&rank, r, (int *)&rank, &info);
+ /* error checking */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", TRTRI) " in ", LMLEC_ELIM) "()\n", -info);
+ }
+ else if(info>0){
+ fprintf(stderr, RCAT(RCAT("A(%d, %d) is exactly zero for ", TRTRI) " (singular matrix) in ", LMLEC_ELIM) "()\n", info, info);
+ }
+ free(buf);
+ return LM_ERROR;
+ }
+ /* then, transpose r in place */
+ for(i=0; i<rank; ++i)
+ for(j=i+1; j<rank; ++j){
+ tmp=r[i+j*rank];
+ k=j+i*rank;
+ r[i+j*rank]=r[k];
+ r[k]=tmp;
+ }
+
+ /* finally, permute R^-T using Y as intermediate storage */
+ for(j=0; j<rank; ++j)
+ for(i=0, k=jpvt[j]-1; i<rank; ++i)
+ Y[i+k*rank]=r[i+j*rank];
+
+ for(i=0; i<rank*rank; ++i) // copy back to r
+ r[i]=Y[i];
+
+ /* resize a to be tm x tm, filling with zeroes */
+ for(i=tm*tn; i<tm*tm; ++i)
+ a[i]=0.0;
+
+ /* compute Q in a as the product of elementary reflectors. Q is tm x tm */
+ ORGQR((int *)&tm, (int *)&tm, (int *)&mintmn, a, (int *)&tm, tau, work, &worksz, &info);
+ /* error checking */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", ORGQR) " in ", LMLEC_ELIM) "()\n", -info);
+ }
+ else if(info>0){
+ fprintf(stderr, RCAT(RCAT("unknown LAPACK error (%d) for ", ORGQR) " in ", LMLEC_ELIM) "()\n", info);
+ }
+ free(buf);
+ return LM_ERROR;
+ }
+
+ /* compute Y=Q_1*R^-T*P^T. Y is tm x rank */
+ for(i=0; i<tm; ++i)
+ for(j=0; j<rank; ++j){
+ for(k=0, tmp=0.0; k<rank; ++k)
+ tmp+=a[i+k*tm]*r[k+j*rank];
+ Y[i*rank+j]=tmp;
+ }
+
+ if(b && c){
+ /* compute c=Y*b */
+ for(i=0; i<tm; ++i){
+ for(j=0, tmp=0.0; j<rank; ++j)
+ tmp+=Y[i*rank+j]*b[j];
+
+ c[i]=tmp;
+ }
+ }
+
+ /* copy Q_2 into Z. Z is tm x (tm-rank) */
+ for(j=0; j<tm-rank; ++j)
+ for(i=0, k=j+rank; i<tm; ++i)
+ Z[i*(tm-rank)+j]=a[i+k*tm];
+
+ free(buf);
+
+ return rank;
+}
+
+/* constrained measurements: given pp, compute the measurements at c + Z*pp */
+static void LMLEC_FUNC(LM_REAL *pp, LM_REAL *hx, int mm, int n, void *adata)
+{
+struct LMLEC_DATA *data=(struct LMLEC_DATA *)adata;
+int m;
+register int i, j;
+register LM_REAL sum;
+LM_REAL *c, *Z, *p, *Zimm;
+
+ m=mm+data->ncnstr;
+ c=data->c;
+ Z=data->Z;
+ p=data->p;
+ /* p=c + Z*pp */
+ for(i=0; i<m; ++i){
+ Zimm=Z+i*mm;
+ for(j=0, sum=c[i]; j<mm; ++j)
+ sum+=Zimm[j]*pp[j]; // sum+=Z[i*mm+j]*pp[j];
+ p[i]=sum;
+ }
+
+ (*(data->func))(p, hx, m, n, data->adata);
+}
+
+/* constrained Jacobian: given pp, compute the Jacobian at c + Z*pp
+ * Using the chain rule, the Jacobian with respect to pp equals the
+ * product of the Jacobian with respect to p (at c + Z*pp) times Z
+ */
+static void LMLEC_JACF(LM_REAL *pp, LM_REAL *jacjac, int mm, int n, void *adata)
+{
+struct LMLEC_DATA *data=(struct LMLEC_DATA *)adata;
+int m;
+register int i, j, l;
+register LM_REAL sum, *aux1, *aux2;
+LM_REAL *c, *Z, *p, *jac;
+
+ m=mm+data->ncnstr;
+ c=data->c;
+ Z=data->Z;
+ p=data->p;
+ jac=data->jac;
+ /* p=c + Z*pp */
+ for(i=0; i<m; ++i){
+ aux1=Z+i*mm;
+ for(j=0, sum=c[i]; j<mm; ++j)
+ sum+=aux1[j]*pp[j]; // sum+=Z[i*mm+j]*pp[j];
+ p[i]=sum;
+ }
+
+ (*(data->jacf))(p, jac, m, n, data->adata);
+
+ /* compute jac*Z in jacjac */
+ if(n*m<=__BLOCKSZ__SQ){ // this is a small problem
+ /* This is the straightforward way to compute jac*Z. However, due to
+ * its noncontinuous memory access pattern, it incures many cache misses when
+ * applied to large minimization problems (i.e. problems involving a large
+ * number of free variables and measurements), in which jac is too large to
+ * fit in the L1 cache. For such problems, a cache-efficient blocking scheme
+ * is preferable. On the other hand, the straightforward algorithm is faster
+ * on small problems since in this case it avoids the overheads of blocking.
+ */
+
+ for(i=0; i<n; ++i){
+ aux1=jac+i*m;
+ aux2=jacjac+i*mm;
+ for(j=0; j<mm; ++j){
+ for(l=0, sum=0.0; l<m; ++l)
+ sum+=aux1[l]*Z[l*mm+j]; // sum+=jac[i*m+l]*Z[l*mm+j];
+
+ aux2[j]=sum; // jacjac[i*mm+j]=sum;
+ }
+ }
+ }
+ else{ // this is a large problem
+ /* Cache efficient computation of jac*Z based on blocking
+ */
+#define __MIN__(x, y) (((x)<=(y))? (x) : (y))
+ register int jj, ll;
+
+ for(jj=0; jj<mm; jj+=__BLOCKSZ__){
+ for(i=0; i<n; ++i){
+ aux1=jacjac+i*mm;
+ for(j=jj; j<__MIN__(jj+__BLOCKSZ__, mm); ++j)
+ aux1[j]=0.0; //jacjac[i*mm+j]=0.0;
+ }
+
+ for(ll=0; ll<m; ll+=__BLOCKSZ__){
+ for(i=0; i<n; ++i){
+ aux1=jacjac+i*mm; aux2=jac+i*m;
+ for(j=jj; j<__MIN__(jj+__BLOCKSZ__, mm); ++j){
+ sum=0.0;
+ for(l=ll; l<__MIN__(ll+__BLOCKSZ__, m); ++l)
+ sum+=aux2[l]*Z[l*mm+j]; //jac[i*m+l]*Z[l*mm+j];
+ aux1[j]+=sum; //jacjac[i*mm+j]+=sum;
+ }
+ }
+ }
+ }
+ }
+}
+#undef __MIN__
+
+
+/*
+ * This function is similar to LEVMAR_DER except that the minimization
+ * is performed subject to the linear constraints A p=b, A is kxm, b kx1
+ *
+ * This function requires an analytic Jacobian. In case the latter is unavailable,
+ * use LEVMAR_LEC_DIF() bellow
+ *
+ */
+int LEVMAR_LEC_DER(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata), /* function to evaluate the Jacobian \part x / \part p */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *A, /* I: constraints matrix, kxm */
+ LM_REAL *b, /* I: right hand constraints vector, kx1 */
+ int k, /* I: number of constraints (i.e. A's #rows) */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[4], /* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3]. Respectively the scale factor for initial \mu,
+ * stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2. Set to NULL for defaults to be used
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_LEC_DER_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func & jacf.
+ * Set to NULL if not needed
+ */
+{
+ struct LMLEC_DATA data;
+ LM_REAL *ptr, *Z, *pp, *p0, *Zimm; /* Z is mxmm */
+ int mm, ret;
+ register int i, j;
+ register LM_REAL tmp;
+ LM_REAL locinfo[LM_INFO_SZ];
+
+ if(!jacf){
+ fprintf(stderr, RCAT("No function specified for computing the Jacobian in ", LEVMAR_LEC_DER)
+ RCAT("().\nIf no such function is available, use ", LEVMAR_LEC_DIF) RCAT("() rather than ", LEVMAR_LEC_DER) "()\n");
+ return LM_ERROR;
+ }
+
+ mm=m-k;
+
+ if(n<mm){
+ fprintf(stderr, LCAT(LEVMAR_LEC_DER, "(): cannot solve a problem with fewer measurements + equality constraints [%d + %d] than unknowns [%d]\n"), n, k, m);
+ return LM_ERROR;
+ }
+
+ ptr=(LM_REAL *)malloc((2*m + m*mm + n*m + mm)*sizeof(LM_REAL));
+ if(!ptr){
+ fprintf(stderr, LCAT(LEVMAR_LEC_DER, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ data.p=p;
+ p0=ptr;
+ data.c=p0+m;
+ data.Z=Z=data.c+m;
+ data.jac=data.Z+m*mm;
+ pp=data.jac+n*m;
+ data.ncnstr=k;
+ data.func=func;
+ data.jacf=jacf;
+ data.adata=adata;
+
+ ret=LMLEC_ELIM(A, b, data.c, NULL, Z, k, m); // compute c, Z
+ if(ret==LM_ERROR){
+ free(ptr);
+ return LM_ERROR;
+ }
+
+ /* compute pp s.t. p = c + Z*pp or (Z^T Z)*pp=Z^T*(p-c)
+ * Due to orthogonality, Z^T Z = I and the last equation
+ * becomes pp=Z^T*(p-c). Also, save the starting p in p0
+ */
+ for(i=0; i<m; ++i){
+ p0[i]=p[i];
+ p[i]-=data.c[i];
+ }
+
+ /* Z^T*(p-c) */
+ for(i=0; i<mm; ++i){
+ for(j=0, tmp=0.0; j<m; ++j)
+ tmp+=Z[j*mm+i]*p[j];
+ pp[i]=tmp;
+ }
+
+ /* compute the p corresponding to pp (i.e. c + Z*pp) and compare with p0 */
+ for(i=0; i<m; ++i){
+ Zimm=Z+i*mm;
+ for(j=0, tmp=data.c[i]; j<mm; ++j)
+ tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
+ if(FABS(tmp-p0[i])>LM_CNST(1E-03))
+ fprintf(stderr, RCAT("Warning: component %d of starting point not feasible in ", LEVMAR_LEC_DER) "()! [%.10g reset to %.10g]\n",
+ i, p0[i], tmp);
+ }
+
+ if(!info) info=locinfo; /* make sure that LEVMAR_DER() is called with non-null info */
+ /* note that covariance computation is not requested from LEVMAR_DER() */
+ ret=LEVMAR_DER(LMLEC_FUNC, LMLEC_JACF, pp, x, mm, n, itmax, opts, info, work, NULL, (void *)&data);
+
+ /* p=c + Z*pp */
+ for(i=0; i<m; ++i){
+ Zimm=Z+i*mm;
+ for(j=0, tmp=data.c[i]; j<mm; ++j)
+ tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
+ p[i]=tmp;
+ }
+
+ /* compute the covariance from the Jacobian in data.jac */
+ if(covar){
+ LEVMAR_TRANS_MAT_MAT_MULT(data.jac, covar, n, m); /* covar = J^T J */
+ LEVMAR_COVAR(covar, covar, info[1], m, n);
+ }
+
+ free(ptr);
+
+ return ret;
+}
+
+/* Similar to the LEVMAR_LEC_DER() function above, except that the Jacobian is approximated
+ * with the aid of finite differences (forward or central, see the comment for the opts argument)
+ */
+int LEVMAR_LEC_DIF(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
+ LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
+ LM_REAL *x, /* I: measurement vector. NULL implies a zero vector */
+ int m, /* I: parameter vector dimension (i.e. #unknowns) */
+ int n, /* I: measurement vector dimension */
+ LM_REAL *A, /* I: constraints matrix, kxm */
+ LM_REAL *b, /* I: right hand constraints vector, kx1 */
+ int k, /* I: number of constraints (i.e. A's #rows) */
+ int itmax, /* I: maximum number of iterations */
+ LM_REAL opts[5], /* I: opts[0-3] = minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \delta]. Respectively the
+ * scale factor for initial \mu, stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2 and
+ * the step used in difference approximation to the Jacobian. Set to NULL for defaults to be used.
+ * If \delta<0, the Jacobian is approximated with central differences which are more accurate
+ * (but slower!) compared to the forward differences employed by default.
+ */
+ LM_REAL info[LM_INFO_SZ],
+ /* O: information regarding the minimization. Set to NULL if don't care
+ * info[0]= ||e||_2 at initial p.
+ * info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
+ * info[5]= # iterations,
+ * info[6]=reason for terminating: 1 - stopped by small gradient J^T e
+ * 2 - stopped by small Dp
+ * 3 - stopped by itmax
+ * 4 - singular matrix. Restart from current p with increased mu
+ * 5 - no further error reduction is possible. Restart with increased mu
+ * 6 - stopped by small ||e||_2
+ * 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
+ * info[7]= # function evaluations
+ * info[8]= # Jacobian evaluations
+ * info[9]= # linear systems solved, i.e. # attempts for reducing error
+ */
+ LM_REAL *work, /* working memory at least LM_LEC_DIF_WORKSZ() reals large, allocated if NULL */
+ LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
+ void *adata) /* pointer to possibly additional data, passed uninterpreted to func.
+ * Set to NULL if not needed
+ */
+{
+ struct LMLEC_DATA data;
+ LM_REAL *ptr, *Z, *pp, *p0, *Zimm; /* Z is mxmm */
+ int mm, ret;
+ register int i, j;
+ register LM_REAL tmp;
+ LM_REAL locinfo[LM_INFO_SZ];
+
+ mm=m-k;
+
+ if(n<mm){
+ fprintf(stderr, LCAT(LEVMAR_LEC_DIF, "(): cannot solve a problem with fewer measurements + equality constraints [%d + %d] than unknowns [%d]\n"), n, k, m);
+ return LM_ERROR;
+ }
+
+ ptr=(LM_REAL *)malloc((2*m + m*mm + mm)*sizeof(LM_REAL));
+ if(!ptr){
+ fprintf(stderr, LCAT(LEVMAR_LEC_DIF, "(): memory allocation request failed\n"));
+ return LM_ERROR;
+ }
+ data.p=p;
+ p0=ptr;
+ data.c=p0+m;
+ data.Z=Z=data.c+m;
+ data.jac=NULL;
+ pp=data.Z+m*mm;
+ data.ncnstr=k;
+ data.func=func;
+ data.jacf=NULL;
+ data.adata=adata;
+
+ ret=LMLEC_ELIM(A, b, data.c, NULL, Z, k, m); // compute c, Z
+ if(ret==LM_ERROR){
+ free(ptr);
+ return LM_ERROR;
+ }
+
+ /* compute pp s.t. p = c + Z*pp or (Z^T Z)*pp=Z^T*(p-c)
+ * Due to orthogonality, Z^T Z = I and the last equation
+ * becomes pp=Z^T*(p-c). Also, save the starting p in p0
+ */
+ for(i=0; i<m; ++i){
+ p0[i]=p[i];
+ p[i]-=data.c[i];
+ }
+
+ /* Z^T*(p-c) */
+ for(i=0; i<mm; ++i){
+ for(j=0, tmp=0.0; j<m; ++j)
+ tmp+=Z[j*mm+i]*p[j];
+ pp[i]=tmp;
+ }
+
+ /* compute the p corresponding to pp (i.e. c + Z*pp) and compare with p0 */
+ for(i=0; i<m; ++i){
+ Zimm=Z+i*mm;
+ for(j=0, tmp=data.c[i]; j<mm; ++j)
+ tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
+ if(FABS(tmp-p0[i])>LM_CNST(1E-03))
+ fprintf(stderr, RCAT("Warning: component %d of starting point not feasible in ", LEVMAR_LEC_DIF) "()! [%.10g reset to %.10g]\n",
+ i, p0[i], tmp);
+ }
+
+ if(!info) info=locinfo; /* make sure that LEVMAR_DIF() is called with non-null info */
+ /* note that covariance computation is not requested from LEVMAR_DIF() */
+ ret=LEVMAR_DIF(LMLEC_FUNC, pp, x, mm, n, itmax, opts, info, work, NULL, (void *)&data);
+
+ /* p=c + Z*pp */
+ for(i=0; i<m; ++i){
+ Zimm=Z+i*mm;
+ for(j=0, tmp=data.c[i]; j<mm; ++j)
+ tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
+ p[i]=tmp;
+ }
+
+ /* compute the Jacobian with finite differences and use it to estimate the covariance */
+ if(covar){
+ LM_REAL *hx, *wrk, *jac;
+
+ hx=(LM_REAL *)malloc((2*n+n*m)*sizeof(LM_REAL));
+ if(!hx){
+ fprintf(stderr, LCAT(LEVMAR_LEC_DIF, "(): memory allocation request failed\n"));
+ free(ptr);
+ return LM_ERROR;
+ }
+
+ wrk=hx+n;
+ jac=wrk+n;
+
+ (*func)(p, hx, m, n, adata); /* evaluate function at p */
+ LEVMAR_FDIF_FORW_JAC_APPROX(func, p, hx, wrk, (LM_REAL)LM_DIFF_DELTA, jac, m, n, adata); /* compute the Jacobian at p */
+ LEVMAR_TRANS_MAT_MAT_MULT(jac, covar, n, m); /* covar = J^T J */
+ LEVMAR_COVAR(covar, covar, info[1], m, n);
+ free(hx);
+ }
+
+ free(ptr);
+
+ return ret;
+}
+
+/* undefine all. THIS MUST REMAIN AT THE END OF THE FILE */
+#undef LMLEC_DATA
+#undef LMLEC_ELIM
+#undef LMLEC_FUNC
+#undef LMLEC_JACF
+#undef LEVMAR_FDIF_FORW_JAC_APPROX
+#undef LEVMAR_COVAR
+#undef LEVMAR_TRANS_MAT_MAT_MULT
+#undef LEVMAR_LEC_DER
+#undef LEVMAR_LEC_DIF
+#undef LEVMAR_DER
+#undef LEVMAR_DIF
+
+#undef GEQP3
+#undef ORGQR
+#undef TRTRI
diff --git a/sci-libs/levmar/levmar-2.5/matlab/CMakeLists.txt b/sci-libs/levmar/levmar-2.5/matlab/CMakeLists.txt
new file mode 100644
index 000000000..3f529f641
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/CMakeLists.txt
@@ -0,0 +1,58 @@
+# CMake file for levmar's MEX-file; see http://www.cmake.org
+# Requires FindMatlab.cmake included with cmake
+
+PROJECT(LEVMARMEX)
+#CMAKE_MINIMUM_REQUIRED(VERSION 1.4)
+
+INCLUDE("C:/Program Files/CMake 2.4/share/cmake-2.4/Modules/FindMatlab.cmake")
+
+# f2c is sometimes equivalent to libF77 & libI77; in that case, set HAVE_F2C to 0
+SET(HAVE_F2C 1 CACHE BOOL "Do we have f2c or F77/I77?" )
+
+# the directory where the lapack/blas/f2c libraries reside
+SET(LAPACKBLAS_DIR /usr/lib CACHE PATH "Path to lapack/blas libraries")
+
+# the directory where levmar.h resides
+SET(LM_H_DIR .. CACHE PATH "Path to levmar.h")
+# the directory where the levmar library resides
+SET(LEVMAR_DIR .. CACHE PATH "Path to levmar library")
+
+# actual names for the lapack/blas/f2c libraries
+SET(LAPACK_LIB lapack CACHE STRING "The name of the lapack library")
+SET(BLAS_LIB blas CACHE STRING "The name of the blas library")
+IF(HAVE_F2C)
+ SET(F2C_LIB f2c CACHE STRING "The name of the f2c library")
+ELSE(HAVE_F2C)
+ SET(F77_LIB libF77 CACHE STRING "The name of the F77 library")
+ SET(I77_LIB libI77 CACHE STRING "The name of the I77 library")
+ENDIF(HAVE_F2C)
+
+########################## NO CHANGES BEYOND THIS POINT ##########################
+
+INCLUDE_DIRECTORIES(${LM_H_DIR})
+LINK_DIRECTORIES(${LAPACKBLAS_DIR} ${LEVMAR_DIR})
+
+SET(SRC levmar.c)
+
+# naming conventions for the generated file's suffix
+IF(WIN32)
+ SET(SUFFIX ".mexw32")
+ELSE(WIN32)
+ SET(SUFFIX ".mexglx")
+ENDIF(WIN32)
+
+SET(OUTNAME "levmar${SUFFIX}")
+
+ADD_LIBRARY(${OUTNAME} MODULE ${SRC})
+
+IF(HAVE_F2C)
+ ADD_CUSTOM_COMMAND(OUTPUT ${OUTNAME}
+ DEPENDS ${SRC}
+ COMMAND mex
+ ARGS -O -I${LM_H_DIR} ${SRC} -I${MATLAB_INCLUDE_DIR} -L${LAPACKBLAS_DIR} -L${LEVMAR_DIR} -L${MATLAB_MEX_LIBRARY} -llevmar -l${LAPACK_LIB} -l${BLAS_LIB} -l${F2C_LIB} -output ${MATLAB_LIBRARIES} ${OUTNAME})
+ELSE(HAVE_F2C)
+ ADD_CUSTOM_COMMAND(OUTPUT ${OUTNAME}
+ DEPENDS ${SRC}
+ COMMAND mex
+ ARGS -O -I${LM_H_DIR} ${SRC} -I${MATLAB_INCLUDE_DIR} -L${LAPACKBLAS_DIR} -L${LEVMAR_DIR} -L${MATLAB_MEX_LIBRARY} -llevmar -l${LAPACK_LIB} -l${BLAS_LIB} -l${F77_LIB} -l${I77_LIB} ${MATLAB_LIBRARIES} -output ${OUTNAME})
+ENDIF(HAVE_F2C)
diff --git a/sci-libs/levmar/levmar-2.5/matlab/Makefile b/sci-libs/levmar/levmar-2.5/matlab/Makefile
new file mode 100644
index 000000000..024dff3aa
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/Makefile
@@ -0,0 +1,30 @@
+#
+# Unix/Linux Makefile for MATLAB interface to levmar
+#
+
+MEX=mex
+MEXCFLAGS=-I.. -O #-g
+# WHEN USING LAPACK, CHANGE THE NEXT TWO LINES TO WHERE YOUR COMPILED LAPACK/BLAS & F2C LIBS ARE!
+LAPACKBLASLIBS_PATH=/usr/lib
+F2CLIBS_PATH=/usr/local/lib
+
+
+# I had to specify the absolute path to the libs, otherwise mex linked against their dynamic versions...
+INTFACESRCS=levmar.c
+LAPACKLIBS=$(LAPACKBLASLIBS_PATH)/liblapack.a $(LAPACKBLASLIBS_PATH)/libblas.a $(F2CLIBS_PATH)/libf2c.a
+ # On systems with a FORTRAN (not f2c'ed) version of LAPACK, libf2c.a is
+ # not necessary; on others, libf2c.a comes in two parts: libF77.a and libI77.a
+
+LIBS=$(LAPACKLIBS)
+
+dummy: $(INTFACESRCS)
+ $(MEX) $(MEXCFLAGS) $(INTFACESRCS) ../liblevmar.a $(LIBS)
+
+clean:
+ @rm -f levmar.mexglx
+
+depend:
+ makedepend -f Makefile $(INTFACESRCS)
+
+# DO NOT DELETE THIS LINE -- make depend depends on it.
+
diff --git a/sci-libs/levmar/levmar-2.5/matlab/Makefile.w32 b/sci-libs/levmar/levmar-2.5/matlab/Makefile.w32
new file mode 100644
index 000000000..758d8f90e
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/Makefile.w32
@@ -0,0 +1,26 @@
+#
+# Windows Makefile for MATLAB interface to levmar
+#
+
+MEX=mex
+MEXCFLAGS=-I.. -O #-g
+# WHEN USING LAPACK, CHANGE THE NEXT TWO LINES TO WHERE YOUR COMPILED LAPACK/BLAS & F2C LIBS ARE!
+LAPACKBLASLIBS_PATH=C:\src\lib
+F2CLIBS_PATH=$(LAPACKBLASLIBS_PATH) # define appropriately if not identical to LAPACKBLASLIBS_PATH
+
+
+INTFACESRCS=levmar.c
+LAPACKLIBS=$(LAPACKBLASLIBS_PATH)/clapack.lib $(LAPACKBLASLIBS_PATH)/blas.lib $(F2CLIBS_PATH)/libF77.lib $(F2CLIBS_PATH)/libI77.lib
+LIBS=$(LAPACKLIBS)
+
+dummy: $(INTFACESRCS)
+ $(MEX) $(MEXCFLAGS) $(INTFACESRCS) ../levmar.lib $(LIBS)
+
+clean:
+ -del levmar.mexw32
+
+depend:
+ makedepend -f Makefile $(INTFACESRCS)
+
+# DO NOT DELETE THIS LINE -- make depend depends on it.
+
diff --git a/sci-libs/levmar/levmar-2.5/matlab/README.txt b/sci-libs/levmar/levmar-2.5/matlab/README.txt
new file mode 100644
index 000000000..cdc84c9a4
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/README.txt
@@ -0,0 +1,35 @@
+This directory contains a matlab MEX interface to levmar. This interface
+has been tested with Matlab v. 6.5 R13 under linux and v. 7.4 R2007 under Windows.
+Users have also reported success with GNU Octave.
+
+FILES
+The following files are included:
+levmar.c: C MEX-file for levmar
+Makefile: UNIX makefile for compiling levmar.c using mex
+Makefile.w32: Windows makefile for compiling levmar.c using mex
+levmar.m: Documentation for the MEX interface
+lmdemo.m: Demonstration of using the MEX interface; run as matlab < lmdemo.m
+
+*.m: Matlab functions implementing various objective functions and their Jacobians.
+ For instance, meyer.m implements the objective function for Meyer's (reformulated)
+ problem and jacmeyer.m implements its Jacobian.
+
+
+
+COMPILING
+Use the provided Makefile or Makefile.w32, depending on your platform.
+Alternatively, levmar.c can be compiled from matlab's prompt with a
+command like
+
+mex -DHAVE_LAPACK -I.. -O -L<levmar library dir> -L<blas/lapack libraries dir> levmar.c -llevmar -lclapack -lblas -lf2c
+
+Make sure that you substitute the angle brackets with the correct paths to
+the levmar and the blas/lapack directories. Also, on certain systems,
+-lf2c should be changed to -llibF77 -llibI77
+If your mex compiler has not been configured, the following command should be run first:
+
+mex -setup
+
+
+TESTING
+After compiling, execute lmdemo.m with matlab < lmdemo.m
diff --git a/sci-libs/levmar/levmar-2.5/matlab/bt3.m b/sci-libs/levmar/levmar-2.5/matlab/bt3.m
new file mode 100644
index 000000000..a8e4773e1
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/bt3.m
@@ -0,0 +1,11 @@
+function x = bt3(p, adata)
+ n=5;
+
+ t1=p(1)-p(2);
+ t2=p(2)+p(3)-2.0;
+ t3=p(4)-1.0;
+ t4=p(5)-1.0;
+
+ for i=1:n
+ x(i)=t1*t1 + t2*t2 + t3*t3 + t4*t4;
+ end
diff --git a/sci-libs/levmar/levmar-2.5/matlab/expfit.m b/sci-libs/levmar/levmar-2.5/matlab/expfit.m
new file mode 100644
index 000000000..eeb37fcce
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/expfit.m
@@ -0,0 +1,8 @@
+function x = expfit(p, data)
+ n=data;
+
+% data1, data2 are actually unused
+
+ for i=1:n
+ x(i)=p(1)*exp(-p(2)*i) + p(3);
+ end
diff --git a/sci-libs/levmar/levmar-2.5/matlab/hs01.m b/sci-libs/levmar/levmar-2.5/matlab/hs01.m
new file mode 100644
index 000000000..c6912f205
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/hs01.m
@@ -0,0 +1,6 @@
+function x = hs01(p)
+ n=2;
+
+ t=p(1)*p(1);
+ x(1)=10.0*(p(2)-t);
+ x(2)=1.0-p(1);
diff --git a/sci-libs/levmar/levmar-2.5/matlab/jacbt3.m b/sci-libs/levmar/levmar-2.5/matlab/jacbt3.m
new file mode 100644
index 000000000..64383f272
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/jacbt3.m
@@ -0,0 +1,13 @@
+function jac = jacbt3(p, adata)
+ n=5;
+ m=5;
+
+ t1=p(1)-p(2);
+ t2=p(2)+p(3)-2.0;
+ t3=p(4)-1.0;
+ t4=p(5)-1.0;
+
+ for i=1:n
+ jac(i, 1:m)=[2.0*t1, 2.0*(t2-t1), 2.0*t2, 2.0*t3, 2.0*t4];
+ end
+
diff --git a/sci-libs/levmar/levmar-2.5/matlab/jacexpfit.m b/sci-libs/levmar/levmar-2.5/matlab/jacexpfit.m
new file mode 100644
index 000000000..978c7b7ce
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/jacexpfit.m
@@ -0,0 +1,7 @@
+function jac = jacexpfit(p, data)
+ n=data;
+ m=max(size(p));
+
+ for i=1:n
+ jac(i, 1:m)=[exp(-p(2)*i), -p(1)*i*exp(-p(2)*i), 1.0];
+ end
diff --git a/sci-libs/levmar/levmar-2.5/matlab/jachs01.m b/sci-libs/levmar/levmar-2.5/matlab/jachs01.m
new file mode 100644
index 000000000..e08e5e33e
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/jachs01.m
@@ -0,0 +1,5 @@
+function jac = jachs01(p)
+ m=2;
+
+ jac(1, 1:m)=[-20.0*p(1), 10.0];
+ jac(2, 1:m)=[-1.0, 0.0];
diff --git a/sci-libs/levmar/levmar-2.5/matlab/jacmeyer.m b/sci-libs/levmar/levmar-2.5/matlab/jacmeyer.m
new file mode 100644
index 000000000..475d361a1
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/jacmeyer.m
@@ -0,0 +1,10 @@
+function jac = jacmeyer(p, data1, data2)
+ n=16;
+ m=3;
+
+ for i=1:n
+ ui=0.45+0.05*i;
+ tmp=exp(10.0*p(2)/(ui+p(3)) - 13.0);
+
+ jac(i, 1:m)=[tmp, 10.0*p(1)*tmp/(ui+p(3)), -10.0*p(1)*p(2)*tmp/((ui+p(3))*(ui+p(3)))];
+ end
diff --git a/sci-libs/levmar/levmar-2.5/matlab/jacmodhs52.m b/sci-libs/levmar/levmar-2.5/matlab/jacmodhs52.m
new file mode 100644
index 000000000..2be492490
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/jacmodhs52.m
@@ -0,0 +1,7 @@
+function jac = jacmodhs52(p)
+ m=5;
+
+ jac(1, 1:m)=[4.0, -1.0, 0.0, 0.0, 0.0];
+ jac(2, 1:m)=[0.0, 1.0, 1.0, 0.0, 0.0];
+ jac(3, 1:m)=[0.0, 0.0, 0.0, 1.0, 0.0];
+ jac(4, 1:m)=[0.0, 0.0, 0.0, 0.0, 1.0];
diff --git a/sci-libs/levmar/levmar-2.5/matlab/jacmodhs76.m b/sci-libs/levmar/levmar-2.5/matlab/jacmodhs76.m
new file mode 100644
index 000000000..5ffb3dc98
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/jacmodhs76.m
@@ -0,0 +1,7 @@
+function jac = jacmodhs76(p)
+ m=4;
+
+ jac(1, 1:m)=[1.0, 0.0, 0.0, 0.0];
+ jac(2, 1:m)=[0.0, sqrt(0.5), 0.0, 0.0];
+ jac(3, 1:m)=[0.0, 0.0, 1.0, 0.0];
+ jac(4, 1:m)=[0.0, 0.0, 0.0, sqrt(0.5)];
diff --git a/sci-libs/levmar/levmar-2.5/matlab/jacosborne.m b/sci-libs/levmar/levmar-2.5/matlab/jacosborne.m
new file mode 100644
index 000000000..22082c6ef
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/jacosborne.m
@@ -0,0 +1,11 @@
+function jac = jacosborne(p)
+ n=33;
+ m=5;
+
+ for i=1:n
+ t=10*(i-1);
+ tmp1=exp(-p(4)*t);
+ tmp2=exp(-p(5)*t);
+
+ jac(i, 1:m)=[1.0, tmp1, tmp2, -p(2)*t*tmp1, -p(3)*t*tmp2];
+ end
diff --git a/sci-libs/levmar/levmar-2.5/matlab/levmar.c b/sci-libs/levmar/levmar-2.5/matlab/levmar.c
new file mode 100644
index 000000000..b2fb6a216
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/levmar.c
@@ -0,0 +1,677 @@
+/* ////////////////////////////////////////////////////////////////////////////////
+//
+// Matlab MEX file for the Levenberg - Marquardt minimization algorithm
+// Copyright (C) 2007 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+//////////////////////////////////////////////////////////////////////////////// */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <stdarg.h>
+#include <math.h>
+#include <string.h>
+#include <ctype.h>
+
+#include <levmar.h>
+
+#include <mex.h>
+
+/**
+#define DEBUG
+**/
+
+#ifndef HAVE_LAPACK
+#ifdef _MSC_VER
+#pragma message("LAPACK not available, certain functionalities cannot be compiled!")
+#else
+#warning LAPACK not available, certain functionalities cannot be compiled
+#endif /* _MSC_VER */
+#endif /* HAVE_LAPACK */
+
+#define __MAX__(A, B) ((A)>=(B)? (A) : (B))
+
+#define MIN_UNCONSTRAINED 0
+#define MIN_CONSTRAINED_BC 1
+#define MIN_CONSTRAINED_LEC 2
+#define MIN_CONSTRAINED_BLEC 3
+#define MIN_CONSTRAINED_BLEIC 4
+#define MIN_CONSTRAINED_BLIC 5
+#define MIN_CONSTRAINED_LEIC 6
+#define MIN_CONSTRAINED_LIC 7
+
+struct mexdata {
+ /* matlab names of the fitting function & its Jacobian */
+ char *fname, *jacname;
+
+ /* binary flags specifying if input p0 is a row or column vector */
+ int isrow_p0;
+
+ /* rhs args to be passed to matlab. rhs[0] is reserved for
+ * passing the parameter vector. If present, problem-specific
+ * data are passed in rhs[1], rhs[2], etc
+ */
+ mxArray **rhs;
+ int nrhs; /* >= 1 */
+};
+
+/* display printf-style error messages in matlab */
+static void matlabFmtdErrMsgTxt(char *fmt, ...)
+{
+char buf[256];
+va_list args;
+
+ va_start(args, fmt);
+ vsprintf(buf, fmt, args);
+ va_end(args);
+
+ mexErrMsgTxt(buf);
+}
+
+/* display printf-style warning messages in matlab */
+static void matlabFmtdWarnMsgTxt(char *fmt, ...)
+{
+char buf[256];
+va_list args;
+
+ va_start(args, fmt);
+ vsprintf(buf, fmt, args);
+ va_end(args);
+
+ mexWarnMsgTxt(buf);
+}
+
+static void func(double *p, double *hx, int m, int n, void *adata)
+{
+mxArray *lhs[1];
+double *mp, *mx;
+register int i;
+struct mexdata *dat=(struct mexdata *)adata;
+
+ /* prepare to call matlab */
+ mp=mxGetPr(dat->rhs[0]);
+ for(i=0; i<m; ++i)
+ mp[i]=p[i];
+
+ /* invoke matlab */
+ mexCallMATLAB(1, lhs, dat->nrhs, dat->rhs, dat->fname);
+
+ /* copy back results & cleanup */
+ mx=mxGetPr(lhs[0]);
+ for(i=0; i<n; ++i)
+ hx[i]=mx[i];
+
+ /* delete the matrix created by matlab */
+ mxDestroyArray(lhs[0]);
+}
+
+static void jacfunc(double *p, double *j, int m, int n, void *adata)
+{
+mxArray *lhs[1];
+double *mp;
+double *mj;
+register int i, k;
+struct mexdata *dat=(struct mexdata *)adata;
+
+ /* prepare to call matlab */
+ mp=mxGetPr(dat->rhs[0]);
+ for(i=0; i<m; ++i)
+ mp[i]=p[i];
+
+ /* invoke matlab */
+ mexCallMATLAB(1, lhs, dat->nrhs, dat->rhs, dat->jacname);
+
+ /* copy back results & cleanup. Note that the nxm Jacobian
+ * computed by matlab should be transposed so that
+ * levmar gets it in row major, as expected
+ */
+ mj=mxGetPr(lhs[0]);
+ for(i=0; i<n; ++i)
+ for(k=0; k<m; ++k)
+ j[i*m+k]=mj[i+k*n];
+
+ /* delete the matrix created by matlab */
+ mxDestroyArray(lhs[0]);
+}
+
+/* matlab matrices are in column-major, this routine converts them to row major for levmar */
+static double *getTranspose(mxArray *Am)
+{
+int m, n;
+double *At, *A;
+register int i, j;
+
+ m=mxGetM(Am);
+ n=mxGetN(Am);
+ A=mxGetPr(Am);
+ At=mxMalloc(m*n*sizeof(double));
+
+ for(i=0; i<m; i++)
+ for(j=0; j<n; j++)
+ At[i*n+j]=A[i+j*m];
+
+ return At;
+}
+
+/* check the supplied matlab function and its Jacobian. Returns 1 on error, 0 otherwise */
+static int checkFuncAndJacobian(double *p, int m, int n, int havejac, struct mexdata *dat)
+{
+mxArray *lhs[1];
+register int i;
+int ret=0;
+double *mp;
+
+ mexSetTrapFlag(1); /* handle errors in the MEX-file */
+
+ mp=mxGetPr(dat->rhs[0]);
+ for(i=0; i<m; ++i)
+ mp[i]=p[i];
+
+ /* attempt to call the supplied func */
+ i=mexCallMATLAB(1, lhs, dat->nrhs, dat->rhs, dat->fname);
+ if(i){
+ fprintf(stderr, "levmar: error calling '%s'.\n", dat->fname);
+ ret=1;
+ }
+ else if(!mxIsDouble(lhs[0]) || mxIsComplex(lhs[0]) || !(mxGetM(lhs[0])==1 || mxGetN(lhs[0])==1) ||
+ __MAX__(mxGetM(lhs[0]), mxGetN(lhs[0]))!=n){
+ fprintf(stderr, "levmar: '%s' should produce a real vector with %d elements (got %d).\n",
+ dat->fname, n, __MAX__(mxGetM(lhs[0]), mxGetN(lhs[0])));
+ ret=1;
+ }
+ /* delete the matrix created by matlab */
+ mxDestroyArray(lhs[0]);
+
+ if(havejac){
+ /* attempt to call the supplied jac */
+ i=mexCallMATLAB(1, lhs, dat->nrhs, dat->rhs, dat->jacname);
+ if(i){
+ fprintf(stderr, "levmar: error calling '%s'.\n", dat->jacname);
+ ret=1;
+ }
+ else if(!mxIsDouble(lhs[0]) || mxIsComplex(lhs[0]) || mxGetM(lhs[0])!=n || mxGetN(lhs[0])!=m){
+ fprintf(stderr, "levmar: '%s' should produce a real %dx%d matrix (got %dx%d).\n",
+ dat->jacname, n, m, mxGetM(lhs[0]), mxGetN(lhs[0]));
+ ret=1;
+ }
+ else if(mxIsSparse(lhs[0])){
+ fprintf(stderr, "levmar: '%s' should produce a real dense matrix (got a sparse one).\n", dat->jacname);
+ ret=1;
+ }
+ /* delete the matrix created by matlab */
+ mxDestroyArray(lhs[0]);
+ }
+
+ mexSetTrapFlag(0); /* on error terminate the MEX-file and return control to the MATLAB prompt */
+
+ return ret;
+}
+
+
+/*
+[ret, p, info, covar]=levmar_der (f, j, p0, x, itmax, opts, 'unc' ...)
+[ret, p, info, covar]=levmar_bc (f, j, p0, x, itmax, opts, 'bc', lb, ub, ...)
+[ret, p, info, covar]=levmar_lec (f, j, p0, x, itmax, opts, 'lec', A, b, ...)
+[ret, p, info, covar]=levmar_blec(f, j, p0, x, itmax, opts, 'blec', lb, ub, A, b, wghts, ...)
+
+[ret, p, info, covar]=levmar_bleic(f, j, p0, x, itmax, opts, 'bleic', lb, ub, A, b, C, d, ...)
+[ret, p, info, covar]=levmar_blic (f, j, p0, x, itmax, opts, 'blic', lb, ub, C, d, ...)
+[ret, p, info, covar]=levmar_leic (f, j, p0, x, itmax, opts, 'leic', A, b, C, d, ...)
+[ret, p, info, covar]=levmar_lic (f, j, p0, x, itmax, opts, 'lic', C, d, ...)
+
+*/
+
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *Prhs[])
+{
+register int i;
+register double *pdbl;
+mxArray **prhs=(mxArray **)&Prhs[0], *At, *Ct;
+struct mexdata mdata;
+int len, status;
+double *p, *p0, *ret, *x;
+int m, n, havejac, Arows, Crows, itmax, nopts, mintype, nextra;
+double opts[LM_OPTS_SZ]={LM_INIT_MU, LM_STOP_THRESH, LM_STOP_THRESH, LM_STOP_THRESH, LM_DIFF_DELTA};
+double info[LM_INFO_SZ];
+double *lb=NULL, *ub=NULL, *A=NULL, *b=NULL, *wghts=NULL, *C=NULL, *d=NULL, *covar=NULL;
+
+ /* parse input args; start by checking their number */
+ if((nrhs<5))
+ matlabFmtdErrMsgTxt("levmar: at least 5 input arguments required (got %d).", nrhs);
+ if(nlhs>4)
+ matlabFmtdErrMsgTxt("levmar: too many output arguments (max. 4, got %d).", nlhs);
+ else if(nlhs<2)
+ matlabFmtdErrMsgTxt("levmar: too few output arguments (min. 2, got %d).", nlhs);
+
+ /* note that in order to accommodate optional args, prhs & nrhs are adjusted accordingly below */
+
+ /** func **/
+ /* first argument must be a string , i.e. a char row vector */
+ if(mxIsChar(prhs[0])!=1)
+ mexErrMsgTxt("levmar: first argument must be a string.");
+ if(mxGetM(prhs[0])!=1)
+ mexErrMsgTxt("levmar: first argument must be a string (i.e. char row vector).");
+ /* store supplied name */
+ len=mxGetN(prhs[0])+1;
+ mdata.fname=mxCalloc(len, sizeof(char));
+ status=mxGetString(prhs[0], mdata.fname, len);
+ if(status!=0)
+ mexErrMsgTxt("levmar: not enough space. String is truncated.");
+
+ /** jac (optional) **/
+ /* check whether second argument is a string */
+ if(mxIsChar(prhs[1])==1){
+ if(mxGetM(prhs[1])!=1)
+ mexErrMsgTxt("levmar: second argument must be a string (i.e. row vector).");
+ /* store supplied name */
+ len=mxGetN(prhs[1])+1;
+ mdata.jacname=mxCalloc(len, sizeof(char));
+ status=mxGetString(prhs[1], mdata.jacname, len);
+ if(status!=0)
+ mexErrMsgTxt("levmar: not enough space. String is truncated.");
+ havejac=1;
+
+ ++prhs;
+ --nrhs;
+ }
+ else{
+ mdata.jacname=NULL;
+ havejac=0;
+ }
+
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: %s analytic Jacobian\n", havejac? "with" : "no");
+#endif /* DEBUG */
+
+/* CHECK
+if(!mxIsDouble(prhs[1]) || mxIsComplex(prhs[1]) || !(mxGetM(prhs[1])==1 && mxGetN(prhs[1])==1))
+*/
+
+ /** p0 **/
+ /* the second required argument must be a real row or column vector */
+ if(!mxIsDouble(prhs[1]) || mxIsComplex(prhs[1]) || !(mxGetM(prhs[1])==1 || mxGetN(prhs[1])==1))
+ mexErrMsgTxt("levmar: p0 must be a real vector.");
+ p0=mxGetPr(prhs[1]);
+ /* determine if we have a row or column vector and retrieve its
+ * size, i.e. the number of parameters
+ */
+ if(mxGetM(prhs[1])==1){
+ m=mxGetN(prhs[1]);
+ mdata.isrow_p0=1;
+ }
+ else{
+ m=mxGetM(prhs[1]);
+ mdata.isrow_p0=0;
+ }
+ /* copy input parameter vector to avoid destroying it */
+ p=mxMalloc(m*sizeof(double));
+ for(i=0; i<m; ++i)
+ p[i]=p0[i];
+
+ /** x **/
+ /* the third required argument must be a real row or column vector */
+ if(!mxIsDouble(prhs[2]) || mxIsComplex(prhs[2]) || !(mxGetM(prhs[2])==1 || mxGetN(prhs[2])==1))
+ mexErrMsgTxt("levmar: x must be a real vector.");
+ x=mxGetPr(prhs[2]);
+ n=__MAX__(mxGetM(prhs[2]), mxGetN(prhs[2]));
+
+ /** itmax **/
+ /* the fourth required argument must be a scalar */
+ if(!mxIsDouble(prhs[3]) || mxIsComplex(prhs[3]) || mxGetM(prhs[3])!=1 || mxGetN(prhs[3])!=1)
+ mexErrMsgTxt("levmar: itmax must be a scalar.");
+ itmax=(int)mxGetScalar(prhs[3]);
+
+ /** opts **/
+ /* the fifth required argument must be a real row or column vector */
+ if(!mxIsDouble(prhs[4]) || mxIsComplex(prhs[4]) || (!(mxGetM(prhs[4])==1 || mxGetN(prhs[4])==1) &&
+ !(mxGetM(prhs[4])==0 && mxGetN(prhs[4])==0)))
+ mexErrMsgTxt("levmar: opts must be a real vector.");
+ pdbl=mxGetPr(prhs[4]);
+ nopts=__MAX__(mxGetM(prhs[4]), mxGetN(prhs[4]));
+ if(nopts!=0){ /* if opts==[], nothing needs to be done and the defaults are used */
+ if(nopts>LM_OPTS_SZ)
+ matlabFmtdErrMsgTxt("levmar: opts must have at most %d elements, got %d.", LM_OPTS_SZ, nopts);
+ else if(nopts<((havejac)? LM_OPTS_SZ-1 : LM_OPTS_SZ))
+ matlabFmtdWarnMsgTxt("levmar: only the %d first elements of opts specified, remaining set to defaults.", nopts);
+ for(i=0; i<nopts; ++i)
+ opts[i]=pdbl[i];
+ }
+#ifdef DEBUG
+ else{
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: empty options vector, using defaults\n");
+ }
+#endif /* DEBUG */
+
+ /** mintype (optional) **/
+ /* check whether sixth argument is a string */
+ if(nrhs>=6 && mxIsChar(prhs[5])==1 && mxGetM(prhs[5])==1){
+ char *minhowto;
+
+ /* examine supplied name */
+ len=mxGetN(prhs[5])+1;
+ minhowto=mxCalloc(len, sizeof(char));
+ status=mxGetString(prhs[5], minhowto, len);
+ if(status!=0)
+ mexErrMsgTxt("levmar: not enough space. String is truncated.");
+
+ for(i=0; minhowto[i]; ++i)
+ minhowto[i]=tolower(minhowto[i]);
+ if(!strncmp(minhowto, "unc", 3)) mintype=MIN_UNCONSTRAINED;
+ else if(!strncmp(minhowto, "bc", 2)) mintype=MIN_CONSTRAINED_BC;
+ else if(!strncmp(minhowto, "lec", 3)) mintype=MIN_CONSTRAINED_LEC;
+ else if(!strncmp(minhowto, "blec", 4)) mintype=MIN_CONSTRAINED_BLEC;
+ else if(!strncmp(minhowto, "bleic", 5)) mintype=MIN_CONSTRAINED_BLEIC;
+ else if(!strncmp(minhowto, "blic", 4)) mintype=MIN_CONSTRAINED_BLIC;
+ else if(!strncmp(minhowto, "leic", 4)) mintype=MIN_CONSTRAINED_LEIC;
+ else if(!strncmp(minhowto, "lic", 3)) mintype=MIN_CONSTRAINED_BLIC;
+ else matlabFmtdErrMsgTxt("levmar: unknown minimization type '%s'.", minhowto);
+
+ mxFree(minhowto);
+
+ ++prhs;
+ --nrhs;
+ }
+ else
+ mintype=MIN_UNCONSTRAINED;
+
+ if(mintype==MIN_UNCONSTRAINED) goto extraargs;
+
+ /* arguments below this point are optional and their presence depends
+ * upon the minimization type determined above
+ */
+ /** lb, ub **/
+ if(nrhs>=7 && (mintype==MIN_CONSTRAINED_BC || mintype==MIN_CONSTRAINED_BLEC || mintype==MIN_CONSTRAINED_BLIC || mintype==MIN_CONSTRAINED_BLEIC)){
+ /* check if the next two arguments are real row or column vectors */
+ if(mxIsDouble(prhs[5]) && !mxIsComplex(prhs[5]) && (mxGetM(prhs[5])==1 || mxGetN(prhs[5])==1)){
+ if(mxIsDouble(prhs[6]) && !mxIsComplex(prhs[6]) && (mxGetM(prhs[6])==1 || mxGetN(prhs[6])==1)){
+ if((i=__MAX__(mxGetM(prhs[5]), mxGetN(prhs[5])))!=m)
+ matlabFmtdErrMsgTxt("levmar: lb must have %d elements, got %d.", m, i);
+ if((i=__MAX__(mxGetM(prhs[6]), mxGetN(prhs[6])))!=m)
+ matlabFmtdErrMsgTxt("levmar: ub must have %d elements, got %d.", m, i);
+
+ lb=mxGetPr(prhs[5]);
+ ub=mxGetPr(prhs[6]);
+
+ prhs+=2;
+ nrhs-=2;
+ }
+ }
+ }
+
+ /** A, b **/
+ if(nrhs>=7 && (mintype==MIN_CONSTRAINED_LEC || mintype==MIN_CONSTRAINED_BLEC || mintype==MIN_CONSTRAINED_LEIC || mintype==MIN_CONSTRAINED_BLEIC)){
+ /* check if the next two arguments are a real matrix and a real row or column vector */
+ if(mxIsDouble(prhs[5]) && !mxIsComplex(prhs[5]) && mxGetM(prhs[5])>=1 && mxGetN(prhs[5])>=1){
+ if(mxIsDouble(prhs[6]) && !mxIsComplex(prhs[6]) && (mxGetM(prhs[6])==1 || mxGetN(prhs[6])==1)){
+ if((i=mxGetN(prhs[5]))!=m)
+ matlabFmtdErrMsgTxt("levmar: A must have %d columns, got %d.", m, i);
+ if((i=__MAX__(mxGetM(prhs[6]), mxGetN(prhs[6])))!=(Arows=mxGetM(prhs[5])))
+ matlabFmtdErrMsgTxt("levmar: b must have %d elements, got %d.", Arows, i);
+
+ At=prhs[5];
+ b=mxGetPr(prhs[6]);
+ A=getTranspose(At);
+
+ prhs+=2;
+ nrhs-=2;
+ }
+ }
+ }
+
+ /* wghts */
+ /* check if we have a weights vector */
+ if(nrhs>=6 && mintype==MIN_CONSTRAINED_BLEC){ /* only check if we have seen both box & linear constraints */
+ if(mxIsDouble(prhs[5]) && !mxIsComplex(prhs[5]) && (mxGetM(prhs[5])==1 || mxGetN(prhs[5])==1)){
+ if(__MAX__(mxGetM(prhs[5]), mxGetN(prhs[5]))==m){
+ wghts=mxGetPr(prhs[5]);
+
+ ++prhs;
+ --nrhs;
+ }
+ }
+ }
+
+ /** C, d **/
+ if(nrhs>=7 && (mintype==MIN_CONSTRAINED_BLEIC || mintype==MIN_CONSTRAINED_BLIC || mintype==MIN_CONSTRAINED_LEIC || mintype==MIN_CONSTRAINED_LIC)){
+ /* check if the next two arguments are a real matrix and a real row or column vector */
+ if(mxIsDouble(prhs[5]) && !mxIsComplex(prhs[5]) && mxGetM(prhs[5])>=1 && mxGetN(prhs[5])>=1){
+ if(mxIsDouble(prhs[6]) && !mxIsComplex(prhs[6]) && (mxGetM(prhs[6])==1 || mxGetN(prhs[6])==1)){
+ if((i=mxGetN(prhs[5]))!=m)
+ matlabFmtdErrMsgTxt("levmar: C must have %d columns, got %d.", m, i);
+ if((i=__MAX__(mxGetM(prhs[6]), mxGetN(prhs[6])))!=(Crows=mxGetM(prhs[5])))
+ matlabFmtdErrMsgTxt("levmar: d must have %d elements, got %d.", Crows, i);
+
+ Ct=prhs[5];
+ d=mxGetPr(prhs[6]);
+ C=getTranspose(Ct);
+
+ prhs+=2;
+ nrhs-=2;
+ }
+ }
+ }
+
+ /* arguments below this point are assumed to be extra arguments passed
+ * to every invocation of the fitting function and its Jacobian
+ */
+
+extraargs:
+ /* handle any extra args and allocate memory for
+ * passing the current parameter estimate to matlab
+ */
+ nextra=nrhs-5;
+ mdata.nrhs=nextra+1;
+ mdata.rhs=(mxArray **)mxMalloc(mdata.nrhs*sizeof(mxArray *));
+ for(i=0; i<nextra; ++i)
+ mdata.rhs[i+1]=(mxArray *)prhs[nrhs-nextra+i]; /* discard 'const' modifier */
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: %d extra args\n", nextra);
+#endif /* DEBUG */
+
+ if(mdata.isrow_p0){ /* row vector */
+ mdata.rhs[0]=mxCreateDoubleMatrix(1, m, mxREAL);
+ /*
+ mxSetM(mdata.rhs[0], 1);
+ mxSetN(mdata.rhs[0], m);
+ */
+ }
+ else{ /* column vector */
+ mdata.rhs[0]=mxCreateDoubleMatrix(m, 1, mxREAL);
+ /*
+ mxSetM(mdata.rhs[0], m);
+ mxSetN(mdata.rhs[0], 1);
+ */
+ }
+
+ /* ensure that the supplied function & Jacobian are as expected */
+ if(checkFuncAndJacobian(p, m, n, havejac, &mdata)){
+ status=LM_ERROR;
+ goto cleanup;
+ }
+
+ if(nlhs>3) /* covariance output required */
+ covar=mxMalloc(m*m*sizeof(double));
+
+ /* invoke levmar */
+ switch(mintype){
+ case MIN_UNCONSTRAINED: /* no constraints */
+ if(havejac)
+ status=dlevmar_der(func, jacfunc, p, x, m, n, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_dif(func, p, x, m, n, itmax, opts, info, NULL, covar, (void *)&mdata);
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_der()/dlevmar_dif()\n");
+#endif /* DEBUG */
+ break;
+ case MIN_CONSTRAINED_BC: /* box constraints */
+ if(havejac)
+ status=dlevmar_bc_der(func, jacfunc, p, x, m, n, lb, ub, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_bc_dif(func, p, x, m, n, lb, ub, itmax, opts, info, NULL, covar, (void *)&mdata);
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_bc_der()/dlevmar_bc_dif()\n");
+#endif /* DEBUG */
+ break;
+ case MIN_CONSTRAINED_LEC: /* linear equation constraints */
+#ifdef HAVE_LAPACK
+ if(havejac)
+ status=dlevmar_lec_der(func, jacfunc, p, x, m, n, A, b, Arows, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_lec_dif(func, p, x, m, n, A, b, Arows, itmax, opts, info, NULL, covar, (void *)&mdata);
+#else
+ mexErrMsgTxt("levmar: no linear constraints support, HAVE_LAPACK was not defined during MEX-file compilation.");
+#endif /* HAVE_LAPACK */
+
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_lec_der()/dlevmar_lec_dif()\n");
+#endif /* DEBUG */
+ break;
+ case MIN_CONSTRAINED_BLEC: /* box & linear equation constraints */
+#ifdef HAVE_LAPACK
+ if(havejac)
+ status=dlevmar_blec_der(func, jacfunc, p, x, m, n, lb, ub, A, b, Arows, wghts, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_blec_dif(func, p, x, m, n, lb, ub, A, b, Arows, wghts, itmax, opts, info, NULL, covar, (void *)&mdata);
+#else
+ mexErrMsgTxt("levmar: no box & linear constraints support, HAVE_LAPACK was not defined during MEX-file compilation.");
+#endif /* HAVE_LAPACK */
+
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_blec_der()/dlevmar_blec_dif()\n");
+#endif /* DEBUG */
+ break;
+ case MIN_CONSTRAINED_BLEIC: /* box, linear equation & inequalities constraints */
+#ifdef HAVE_LAPACK
+ if(havejac)
+ status=dlevmar_bleic_der(func, jacfunc, p, x, m, n, lb, ub, A, b, Arows, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_bleic_dif(func, p, x, m, n, lb, ub, A, b, Arows, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+#else
+ mexErrMsgTxt("levmar: no box, linear equation & inequality constraints support, HAVE_LAPACK was not defined during MEX-file compilation.");
+#endif /* HAVE_LAPACK */
+
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_bleic_der()/dlevmar_bleic_dif()\n");
+#endif /* DEBUG */
+ break;
+ case MIN_CONSTRAINED_BLIC: /* box, linear inequalities constraints */
+#ifdef HAVE_LAPACK
+ if(havejac)
+ status=dlevmar_bleic_der(func, jacfunc, p, x, m, n, lb, ub, NULL, NULL, 0, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_bleic_dif(func, p, x, m, n, lb, ub, NULL, NULL, 0, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+#else
+ mexErrMsgTxt("levmar: no box & linear inequality constraints support, HAVE_LAPACK was not defined during MEX-file compilation.");
+#endif /* HAVE_LAPACK */
+
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_blic_der()/dlevmar_blic_dif()\n");
+#endif /* DEBUG */
+ break;
+ case MIN_CONSTRAINED_LEIC: /* linear equation & inequalities constraints */
+#ifdef HAVE_LAPACK
+ if(havejac)
+ status=dlevmar_bleic_der(func, jacfunc, p, x, m, n, NULL, NULL, A, b, Arows, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_bleic_dif(func, p, x, m, n, NULL, NULL, A, b, Arows, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+#else
+ mexErrMsgTxt("levmar: no linear equation & inequality constraints support, HAVE_LAPACK was not defined during MEX-file compilation.");
+#endif /* HAVE_LAPACK */
+
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_leic_der()/dlevmar_leic_dif()\n");
+#endif /* DEBUG */
+ break;
+ case MIN_CONSTRAINED_LIC: /* linear inequalities constraints */
+#ifdef HAVE_LAPACK
+ if(havejac)
+ status=dlevmar_bleic_der(func, jacfunc, p, x, m, n, NULL, NULL, NULL, NULL, 0, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+ else
+ status=dlevmar_bleic_dif(func, p, x, m, n, NULL, NULL, NULL, NULL, 0, C, d, Crows, itmax, opts, info, NULL, covar, (void *)&mdata);
+#else
+ mexErrMsgTxt("levmar: no linear equation & inequality constraints support, HAVE_LAPACK was not defined during MEX-file compilation.");
+#endif /* HAVE_LAPACK */
+
+#ifdef DEBUG
+ fflush(stderr);
+ fprintf(stderr, "LEVMAR: calling dlevmar_lic_der()/dlevmar_lic_dif()\n");
+#endif /* DEBUG */
+ break;
+ default:
+ mexErrMsgTxt("levmar: unexpected internal error.");
+ }
+
+#ifdef DEBUG
+ fflush(stderr);
+ printf("LEVMAR: minimization returned %d in %g iter, reason %g\n\tSolution: ", status, info[5], info[6]);
+ for(i=0; i<m; ++i)
+ printf("%.7g ", p[i]);
+ printf("\n\n\tMinimization info:\n\t");
+ for(i=0; i<LM_INFO_SZ; ++i)
+ printf("%g ", info[i]);
+ printf("\n");
+#endif /* DEBUG */
+
+ /* copy back return results */
+ /** ret **/
+ plhs[0]=mxCreateDoubleMatrix(1, 1, mxREAL);
+ ret=mxGetPr(plhs[0]);
+ ret[0]=(double)status;
+
+ /** popt **/
+ plhs[1]=(mdata.isrow_p0==1)? mxCreateDoubleMatrix(1, m, mxREAL) : mxCreateDoubleMatrix(m, 1, mxREAL);
+ pdbl=mxGetPr(plhs[1]);
+ for(i=0; i<m; ++i)
+ pdbl[i]=p[i];
+
+ /** info **/
+ if(nlhs>2){
+ plhs[2]=mxCreateDoubleMatrix(1, LM_INFO_SZ, mxREAL);
+ pdbl=mxGetPr(plhs[2]);
+ for(i=0; i<LM_INFO_SZ; ++i)
+ pdbl[i]=info[i];
+ }
+
+ /** covar **/
+ if(nlhs>3){
+ plhs[3]=mxCreateDoubleMatrix(m, m, mxREAL);
+ pdbl=mxGetPr(plhs[3]);
+ for(i=0; i<m*m; ++i) /* covariance matrices are symmetric, thus no need to transpose! */
+ pdbl[i]=covar[i];
+ }
+
+cleanup:
+ /* cleanup */
+ mxDestroyArray(mdata.rhs[0]);
+ if(A) mxFree(A);
+ if(C) mxFree(C);
+
+ mxFree(mdata.fname);
+ if(havejac) mxFree(mdata.jacname);
+ mxFree(p);
+ mxFree(mdata.rhs);
+ if(covar) mxFree(covar);
+
+ if(status==LM_ERROR)
+ mexWarnMsgTxt("levmar: optimization returned with an error!");
+}
diff --git a/sci-libs/levmar/levmar-2.5/matlab/levmar.m b/sci-libs/levmar/levmar-2.5/matlab/levmar.m
new file mode 100644
index 000000000..3dfaad0c5
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/levmar.m
@@ -0,0 +1,84 @@
+function [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, type)
+% LEVMAR matlab MEX interface to the levmar non-linear least squares minimization
+% library available from http://www.ics.forth.gr/~lourakis/levmar/
+%
+% levmar can be used in any of the 8 following ways:
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'unc', ...)
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'bc', lb, ub, ...)
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'lec', A, b, ...)
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'blec', lb, ub, A, b, wghts, ...)
+%
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'bleic', lb, ub, A, b, C, d, ...)
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'blic', lb, ub, C, d, ...)
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'leic', A, b, C, d, ...)
+% [ret, popt, info, covar]=levmar(fname, jacname, p0, x, itmax, opts, 'lic', C, d, ...)
+%
+%
+% The dots at the end denote any additional, problem specific data that are passed uniterpreted to
+% all invocations of fname and jacname, see below for details.
+%
+% In the following, the word "vector" is meant to imply either a row or a column vector.
+%
+% required input arguments:
+% - fname: String defining the name of a matlab function implementing the function to be minimized.
+% fname will be called as fname(p, ...), where p denotes the parameter vector and the dots any
+% additional data passed as extra arguments during the invocation of levmar (refer to Meyer's
+% problem in lmdemo.m for an example).
+%
+% - p0: vector of doubles holding the initial parameters estimates.
+%
+% - x: vector of doubles holding the measurements vector.
+%
+% - itmax: maximum number of iterations.
+%
+% - opts: vector of doubles specifying the minimization parameters, as follows:
+% opts(1) scale factor for the initial damping factor
+% opts(2) stopping threshold for ||J^T e||_inf
+% opts(3) stopping threshold for ||Dp||_2
+% opts(4) stopping threshold for ||e||_2
+% opts(5) step used in finite difference approximation to the Jacobian.
+% If an empty vector (i.e. []) is specified, defaults are used.
+%
+% optional input arguments:
+% - jacname: String defining the name of matlab function implementing the Jacobian of function fname.
+% jacname will be called as jacname(p, ...) where p is again the parameter vector and the dots
+% denote any additional data passed as extra arguments to the invocation of levmar. If omitted,
+% the Jacobian is approximated with finite differences through repeated invocations of fname.
+%
+% - type: String defining the minimization type. It should be one of the following:
+% 'unc' specifies unconstrained minimization.
+% 'bc' specifies minimization subject to box constraints.
+% 'lec' specifies minimization subject to linear equation constraints.
+% 'blec' specifies minimization subject to box and linear equation constraints.
+% 'bleic' specifies minimization subject to box, linear equation and inequality constraints.
+% 'blic' specifies minimization subject to box and linear inequality constraints.
+% 'leic' specifies minimization subject to linear equation and inequality constraints.
+% 'lic' specifies minimization subject to linear inequality constraints.
+% If omitted, a default of 'unc' is assumed. Depending on the minimization type, the MEX
+% interface will invoke one of dlevmar_XXX, dlevmar_bc_XXX, dlevmar_lec_XXX, dlevmar_blec_XXX or dlevmar_bleic_XXX
+%
+% - lb, ub: vectors of doubles specifying lower and upper bounds for p, respectively
+%
+% - A, b: k x m matrix and k vector specifying linear equation constraints for p, i.e. A*p=b
+% A should have full rank.
+%
+% - C, d: k x m matrix and k vector specifying linear inequality constraints for p, i.e. C*p>=d
+% A should have full rank.
+%
+% - wghts: vector of doubles specifying the weights for the penalty terms corresponding to
+% the box constraints, see lmblec_core.c for more details. If omitted and a 'blec' type
+% minimization is to be carried out, default weights are used.
+%
+%
+% output arguments
+% - ret: return value of levmar, corresponding to the number of iterations if successful, -1 otherwise.
+%
+% - popt: estimated minimizer, i.e. minimized parameters vector.
+%
+% - info: optional array of doubles, which upon return provides information regarding the minimization.
+% See lm_core.c for more details.
+%
+% - covar: optional covariance matrix corresponding to the estimated minimizer.
+%
+
+error('levmar.m is used only for providing documentation to levmar; make sure that levmar.c has been compiled using mex');
diff --git a/sci-libs/levmar/levmar-2.5/matlab/lmdemo.m b/sci-libs/levmar/levmar-2.5/matlab/lmdemo.m
new file mode 100644
index 000000000..d4cb96acc
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/lmdemo.m
@@ -0,0 +1,140 @@
+% Demo program for levmar's MEX-file interface
+% Performs minimization of several test problems
+
+format long;
+
+% Unconstrained minimization
+
+% fitting the exponential model x_i=p(1)*exp(-p(2)*i)+p(3) of expfit.c to noisy measurements obtained with (5.0 0.1 1.0)
+p0=[1.0, 0.0, 0.0];
+x=[5.8728, 5.4948, 5.0081, 4.5929, 4.3574, 4.1198, 3.6843, 3.3642, 2.9742, 3.0237, 2.7002, 2.8781,...
+ 2.5144, 2.4432, 2.2894, 2.0938, 1.9265, 2.1271, 1.8387, 1.7791, 1.6686, 1.6232, 1.571, 1.6057,...
+ 1.3825, 1.5087, 1.3624, 1.4206, 1.2097, 1.3129, 1.131, 1.306, 1.2008, 1.3469, 1.1837, 1.2102,...
+ 0.96518, 1.2129, 1.2003, 1.0743];
+
+options=[1E-03, 1E-15, 1E-15, 1E-20, 1E-06];
+% arg demonstrates additional data passing to expfit/jacexpfit
+arg=[40];
+
+[ret, popt, info]=levmar('expfit', 'jacexpfit', p0, x, 200, options, arg);
+disp('Exponential model fitting (see also ../expfit.c)');
+popt
+
+
+% Meyer's (reformulated) problem
+p0=[8.85, 4.0, 2.5];
+
+x=[];
+x(1:4)=[34.780, 28.610, 23.650, 19.630];
+x(5:8)=[16.370, 13.720, 11.540, 9.744];
+x(9:12)=[8.261, 7.030, 6.005, 5.147];
+x(13:16)=[4.427, 3.820, 3.307, 2.872];
+
+options=[1E-03, 1E-15, 1E-15, 1E-20, 1E-06];
+% arg1, arg2 demonstrate additional dummy data passing to meyer/jacmeyer
+arg1=[17];
+arg2=[27];
+
+%[ret, popt, info]=levmar('meyer', 'jacmeyer', p0, x, 200, options, arg1, arg2);
+
+%[ret, popt, info, covar]=levmar('meyer', 'jacmeyer', p0, x, 200, options, arg1, arg2);
+[ret, popt, info, covar]=levmar('meyer', p0, x, 200, options, 'unc', arg1, arg2);
+disp('Meyer''s (reformulated) problem');
+popt
+
+
+% Osborne's problem
+p0=[0.5, 1.5, -1.0, 1.0E-2, 2.0E-2];
+
+x=[8.44E-1, 9.08E-1, 9.32E-1, 9.36E-1, 9.25E-1, 9.08E-1, 8.81E-1,...
+8.5E-1, 8.18E-1, 7.84E-1, 7.51E-1, 7.18E-1, 6.85E-1, 6.58E-1,...
+6.28E-1, 6.03E-1, 5.8E-1, 5.58E-1, 5.38E-1, 5.22E-1, 5.06E-1,...
+4.9E-1, 4.78E-1, 4.67E-1, 4.57E-1, 4.48E-1, 4.38E-1, 4.31E-1,...
+4.24E-1, 4.2E-1, 4.14E-1, 4.11E-1, 4.06E-1];
+
+
+options=[1E-03, 1E-15, 1E-15, 1E-20, 1E-06];
+
+[ret, popt, info, covar]=levmar('osborne', 'jacosborne', p0, x, 200, options);
+%[ret, popt, info, covar]=levmar('osborne', p0, x, 200, options, 'unc');
+disp('Osborne''s problem');
+popt
+
+
+% Linear constraints
+
+% Boggs-Tolle problem 3
+p0=[2.0, 2.0, 2.0, 2.0, 2.0];
+x=[0.0, 0.0, 0.0, 0.0, 0.0];
+options=[1E-03, 1E-15, 1E-15, 1E-20];
+adata=[];
+
+A=[1.0, 3.0, 0.0, 0.0, 0.0;
+ 0.0, 0.0, 1.0, 1.0, -2.0;
+ 0.0, 1.0, 0.0, 0.0, -1.0];
+b=[0.0, 0.0, 0.0]';
+
+[ret, popt, info, covar]=levmar('bt3', 'jacbt3', p0, x, 200, options, 'lec', A, b, adata);
+disp('Boggs-Tolle problem 3');
+popt
+
+
+% Box constraints
+
+% Hock-Schittkowski problem 01
+p0=[-2.0, 1.0];
+x=[0.0, 0.0];
+lb=[-realmax, -1.5];
+ub=[realmax, realmax];
+options=[1E-03, 1E-15, 1E-15, 1E-20];
+
+[ret, popt, info, covar]=levmar('hs01', 'jachs01', p0, x, 200, options, 'bc', lb, ub);
+disp('Hock-Schittkowski problem 01');
+popt
+
+
+% Box and linear constraints
+
+% Hock-Schittkowski modified problem 52 (#1)
+p0=[2.0, 2.0, 2.0, 2.0, 2.0];
+x=[0.0, 0.0, 0.0, 0.0];
+lb=[-0.09, 0.0, -realmax, -0.2, 0.0];
+ub=[realmax, 0.3, 0.25, 0.3, 0.3];
+A=[1.0, 3.0, 0.0, 0.0, 0.0;
+ 0.0, 0.0, 1.0, 1.0, -2.0;
+ 0.0, 1.0, 0.0, 0.0, -1.0];
+b=[0.0, 0.0, 0.0]';
+options=[1E-03, 1E-15, 1E-15, 1E-20];
+
+[ret, popt, info, covar]=levmar('modhs52', 'jacmodhs52', p0, x, 200, options, 'blec', lb, ub, A, b);
+disp('Hock-Schittkowski modified problem 52 (#1)');
+popt
+
+% Schittkowski modified problem 235
+p0=[-2.0, 3.0, 1.0];
+x=[0.0, 0.0];
+lb=[-realmax, 0.1, 0.7];
+ub=[realmax, 2.9, realmax];
+A=[1.0, 0.0, 1.0;
+ 0.0, 1.0, -4.0];
+b=[-1.0, 0.0]';
+options=[1E-03, 1E-15, 1E-15, 1E-20];
+
+[ret, popt, info, covar]=levmar('mods235', p0, x, 200, options, 'blec', lb, ub, A, b);
+disp('Hock-Schittkowski modified problem 235');
+popt
+
+% Box, linear equation & inequality constraints
+p0=[0.5, 0.5, 0.5, 0.5];
+x=[0.0, 0.0, 0.0, 0.0];
+lb=[0.0, 0.0, 0.0, 0.0];
+ub=[realmax, realmax, realmax, realmax];
+A=[0.0, 1.0, 4.0, 0.0];
+b=[1.5]';
+C=[-1.0, -2.0, -1.0, -1.0;
+ -3.0, -1.0, -2.0, 1.0];
+d=[-5.0, -0.4]';
+
+[ret, popt, info, covar]=levmar('modhs76', 'jacmodhs76', p0, x, 200, options, 'bleic', lb, ub, A, b, C, d);
+disp('Hock-Schittkowski modified problem 76');
+popt
diff --git a/sci-libs/levmar/levmar-2.5/matlab/meyer.m b/sci-libs/levmar/levmar-2.5/matlab/meyer.m
new file mode 100644
index 000000000..3d6b7c989
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/meyer.m
@@ -0,0 +1,9 @@
+function x = meyer(p, data1, data2)
+ n=16;
+
+% data1, data2 are actually unused
+
+ for i=1:n
+ ui=0.45+0.05*i;
+ x(i)=p(1)*exp(10.0*p(2)/(ui+p(3)) - 13.0);
+ end
diff --git a/sci-libs/levmar/levmar-2.5/matlab/modhs52.m b/sci-libs/levmar/levmar-2.5/matlab/modhs52.m
new file mode 100644
index 000000000..db23006ee
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/modhs52.m
@@ -0,0 +1,7 @@
+function x = modhs52(p)
+ n=4;
+
+ x(1)=4.0*p(1)-p(2);
+ x(2)=p(2)+p(3)-2.0;
+ x(3)=p(4)-1.0;
+ x(4)=p(5)-1.0;
diff --git a/sci-libs/levmar/levmar-2.5/matlab/modhs76.m b/sci-libs/levmar/levmar-2.5/matlab/modhs76.m
new file mode 100644
index 000000000..1e4958326
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/modhs76.m
@@ -0,0 +1,7 @@
+function x = modhs76(p)
+
+ x(1)=p(1);
+ x(2)=sqrt(0.5)*p(2);
+ x(3)=p(3);
+ x(4)=sqrt(0.5)*p(4);
+
diff --git a/sci-libs/levmar/levmar-2.5/matlab/mods235.m b/sci-libs/levmar/levmar-2.5/matlab/mods235.m
new file mode 100644
index 000000000..abef5eb82
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/mods235.m
@@ -0,0 +1,5 @@
+function x = mods235(p)
+ n=2;
+
+ x(1)=0.1*(p(1)-1.0);
+ x(2)=p(2)-p(1)*p(1);
diff --git a/sci-libs/levmar/levmar-2.5/matlab/osborne.m b/sci-libs/levmar/levmar-2.5/matlab/osborne.m
new file mode 100644
index 000000000..2d15373ee
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/matlab/osborne.m
@@ -0,0 +1,7 @@
+function x = osborne(p)
+ n=33;
+
+ for i=1:n
+ t=10*(i-1);
+ x(i)=p(1) + p(2)*exp(-p(4)*t) + p(3)*exp(-p(5)*t);
+ end
diff --git a/sci-libs/levmar/levmar-2.5/misc.c b/sci-libs/levmar/levmar-2.5/misc.c
new file mode 100644
index 000000000..1c6e99649
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/misc.c
@@ -0,0 +1,70 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-05 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+/********************************************************************************
+ * Miscelaneous functions for Levenberg-Marquardt nonlinear minimization. The
+ * same core code is used with appropriate #defines to derive single and double
+ * precision versions, see also misc_core.c
+ ********************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <float.h>
+
+#include "levmar.h"
+#include "misc.h"
+
+#if !defined(LM_DBL_PREC) && !defined(LM_SNGL_PREC)
+#error At least one of LM_DBL_PREC, LM_SNGL_PREC should be defined!
+#endif
+
+#ifdef LM_SNGL_PREC
+/* single precision (float) definitions */
+#define LM_REAL float
+#define LM_PREFIX s
+
+#define LM_REAL_EPSILON FLT_EPSILON
+#define __SUBCNST(x) x##F
+#define LM_CNST(x) __SUBCNST(x) // force substitution
+
+#include "misc_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_EPSILON
+#undef __SUBCNST
+#undef LM_CNST
+#endif /* LM_SNGL_PREC */
+
+#ifdef LM_DBL_PREC
+/* double precision definitions */
+#define LM_REAL double
+#define LM_PREFIX d
+
+#define LM_REAL_EPSILON DBL_EPSILON
+#define LM_CNST(x) (x)
+
+#include "misc_core.c" // read in core code
+
+#undef LM_REAL
+#undef LM_PREFIX
+#undef LM_REAL_EPSILON
+#undef LM_CNST
+#endif /* LM_DBL_PREC */
diff --git a/sci-libs/levmar/levmar-2.5/misc.h b/sci-libs/levmar/levmar-2.5/misc.h
new file mode 100644
index 000000000..e32f18de2
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/misc.h
@@ -0,0 +1,106 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+#ifndef _MISC_H_
+#define _MISC_H_
+
+/* common suffix for LAPACK subroutines. Define empty in case of no prefix. */
+#define LM_LAPACK_SUFFIX _
+//#define LM_LAPACK_SUFFIX // define empty
+
+/* common prefix for BLAS subroutines. Leave undefined in case of no prefix.
+ * You might also need to modify LM_BLAS_PREFIX below
+ */
+/* f2c'd BLAS */
+//#define LM_BLAS_PREFIX f2c_
+/* C BLAS */
+//#define LM_BLAS_PREFIX cblas_
+
+/* common suffix for BLAS subroutines */
+//#define LM_BLAS_SUFFIX // define empty if a f2c_ or cblas_ prefix was defined for LM_BLAS_PREFIX above
+#define LM_BLAS_SUFFIX _ // use this in case of no BLAS prefix
+
+
+#define LCAT_(a, b) #a b
+#define LCAT(a, b) LCAT_(a, b) // force substitution
+#define RCAT_(a, b) a #b
+#define RCAT(a, b) RCAT_(a, b) // force substitution
+
+#define LM_MK_LAPACK_NAME(s) LM_ADD_PREFIX(LM_CAT_(s, LM_LAPACK_SUFFIX))
+
+
+#define __BLOCKSZ__ 32 /* block size for cache-friendly matrix-matrix multiply. It should be
+ * such that __BLOCKSZ__^2*sizeof(LM_REAL) is smaller than the CPU (L1)
+ * data cache size. Notice that a value of 32 when LM_REAL=double assumes
+ * an 8Kb L1 data cache (32*32*8=8K). This is a concervative choice since
+ * newer Pentium 4s have a L1 data cache of size 16K, capable of holding
+ * up to 45x45 double blocks.
+ */
+#define __BLOCKSZ__SQ (__BLOCKSZ__)*(__BLOCKSZ__)
+
+/* add a prefix in front of a token */
+#define LM_CAT__(a, b) a ## b
+#define LM_CAT_(a, b) LM_CAT__(a, b) // force substitution
+#define LM_ADD_PREFIX(s) LM_CAT_(LM_PREFIX, s)
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* blocking-based matrix multiply */
+extern void slevmar_trans_mat_mat_mult(float *a, float *b, int n, int m);
+extern void dlevmar_trans_mat_mat_mult(double *a, double *b, int n, int m);
+
+/* forward finite differences */
+extern void slevmar_fdif_forw_jac_approx(void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *hx, float *hxx, float delta,
+ float *jac, int m, int n, void *adata);
+extern void dlevmar_fdif_forw_jac_approx(void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *hx, double *hxx, double delta,
+ double *jac, int m, int n, void *adata);
+
+/* central finite differences */
+extern void slevmar_fdif_cent_jac_approx(void (*func)(float *p, float *hx, int m, int n, void *adata),
+ float *p, float *hxm, float *hxp, float delta,
+ float *jac, int m, int n, void *adata);
+extern void dlevmar_fdif_cent_jac_approx(void (*func)(double *p, double *hx, int m, int n, void *adata),
+ double *p, double *hxm, double *hxp, double delta,
+ double *jac, int m, int n, void *adata);
+
+/* e=x-y and ||e|| */
+extern float slevmar_L2nrmxmy(float *e, float *x, float *y, int n);
+extern double dlevmar_L2nrmxmy(double *e, double *x, double *y, int n);
+
+/* covariance of LS fit */
+extern int slevmar_covar(float *JtJ, float *C, float sumsq, int m, int n);
+extern int dlevmar_covar(double *JtJ, double *C, double sumsq, int m, int n);
+
+/* box constraints consistency check */
+extern int slevmar_box_check(float *lb, float *ub, int m);
+extern int dlevmar_box_check(double *lb, double *ub, int m);
+
+/* Cholesky */
+extern int slevmar_chol(float *C, float *W, int m);
+extern int dlevmar_chol(double *C, double *W, int m);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _MISC_H_ */
diff --git a/sci-libs/levmar/levmar-2.5/misc.o b/sci-libs/levmar/levmar-2.5/misc.o
new file mode 100644
index 000000000..776f03e96
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/misc.o
Binary files differ
diff --git a/sci-libs/levmar/levmar-2.5/misc_core.c b/sci-libs/levmar/levmar-2.5/misc_core.c
new file mode 100644
index 000000000..f76c00e41
--- /dev/null
+++ b/sci-libs/levmar/levmar-2.5/misc_core.c
@@ -0,0 +1,813 @@
+/////////////////////////////////////////////////////////////////////////////////
+//
+// Levenberg - Marquardt non-linear minimization algorithm
+// Copyright (C) 2004-05 Manolis Lourakis (lourakis at ics forth gr)
+// Institute of Computer Science, Foundation for Research & Technology - Hellas
+// Heraklion, Crete, Greece.
+//
+// This program is free software; you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation; either version 2 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+/////////////////////////////////////////////////////////////////////////////////
+
+#ifndef LM_REAL // not included by misc.c
+#error This file should not be compiled directly!
+#endif
+
+
+/* precision-specific definitions */
+#define LEVMAR_CHKJAC LM_ADD_PREFIX(levmar_chkjac)
+#define LEVMAR_FDIF_FORW_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_forw_jac_approx)
+#define LEVMAR_FDIF_CENT_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_cent_jac_approx)
+#define LEVMAR_TRANS_MAT_MAT_MULT LM_ADD_PREFIX(levmar_trans_mat_mat_mult)
+#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
+#define LEVMAR_STDDEV LM_ADD_PREFIX(levmar_stddev)
+#define LEVMAR_CORCOEF LM_ADD_PREFIX(levmar_corcoef)
+#define LEVMAR_R2 LM_ADD_PREFIX(levmar_R2)
+#define LEVMAR_BOX_CHECK LM_ADD_PREFIX(levmar_box_check)
+#define LEVMAR_L2NRMXMY LM_ADD_PREFIX(levmar_L2nrmxmy)
+
+#ifdef HAVE_LAPACK
+#define LEVMAR_PSEUDOINVERSE LM_ADD_PREFIX(levmar_pseudoinverse)
+static int LEVMAR_PSEUDOINVERSE(LM_REAL *A, LM_REAL *B, int m);
+
+/* BLAS matrix multiplication & LAPACK SVD routines */
+#ifdef LM_BLAS_PREFIX
+#define GEMM LM_CAT_(LM_BLAS_PREFIX, LM_ADD_PREFIX(LM_CAT_(gemm, LM_BLAS_SUFFIX)))
+#else
+#define GEMM LM_ADD_PREFIX(LM_CAT_(gemm, LM_BLAS_SUFFIX))
+#endif
+/* C := alpha*op( A )*op( B ) + beta*C */
+extern void GEMM(char *transa, char *transb, int *m, int *n, int *k,
+ LM_REAL *alpha, LM_REAL *a, int *lda, LM_REAL *b, int *ldb, LM_REAL *beta, LM_REAL *c, int *ldc);
+
+#define GESVD LM_MK_LAPACK_NAME(gesvd)
+#define GESDD LM_MK_LAPACK_NAME(gesdd)
+extern int GESVD(char *jobu, char *jobvt, int *m, int *n, LM_REAL *a, int *lda, LM_REAL *s, LM_REAL *u, int *ldu,
+ LM_REAL *vt, int *ldvt, LM_REAL *work, int *lwork, int *info);
+
+/* lapack 3.0 new SVD routine, faster than xgesvd() */
+extern int GESDD(char *jobz, int *m, int *n, LM_REAL *a, int *lda, LM_REAL *s, LM_REAL *u, int *ldu, LM_REAL *vt, int *ldvt,
+ LM_REAL *work, int *lwork, int *iwork, int *info);
+
+/* Cholesky decomposition */
+#define POTF2 LM_MK_LAPACK_NAME(potf2)
+extern int POTF2(char *uplo, int *n, LM_REAL *a, int *lda, int *info);
+
+#define LEVMAR_CHOLESKY LM_ADD_PREFIX(levmar_chol)
+
+#else
+#define LEVMAR_LUINVERSE LM_ADD_PREFIX(levmar_LUinverse_noLapack)
+
+static int LEVMAR_LUINVERSE(LM_REAL *A, LM_REAL *B, int m);
+#endif /* HAVE_LAPACK */
+
+/* blocked multiplication of the transpose of the nxm matrix a with itself (i.e. a^T a)
+ * using a block size of bsize. The product is returned in b.
+ * Since a^T a is symmetric, its computation can be sped up by computing only its
+ * upper triangular part and copying it to the lower part.
+ *
+ * More details on blocking can be found at
+ * http://www-2.cs.cmu.edu/afs/cs/academic/class/15213-f02/www/R07/section_a/Recitation07-SectionA.pdf
+ */
+void LEVMAR_TRANS_MAT_MAT_MULT(LM_REAL *a, LM_REAL *b, int n, int m)
+{
+#ifdef HAVE_LAPACK /* use BLAS matrix multiply */
+
+LM_REAL alpha=LM_CNST(1.0), beta=LM_CNST(0.0);
+ /* Fool BLAS to compute a^T*a avoiding transposing a: a is equivalent to a^T in column major,
+ * therefore BLAS computes a*a^T with a and a*a^T in column major, which is equivalent to
+ * computing a^T*a in row major!
+ */
+ GEMM("N", "T", &m, &m, &n, &alpha, a, &m, a, &m, &beta, b, &m);
+
+#else /* no LAPACK, use blocking-based multiply */
+
+register int i, j, k, jj, kk;
+register LM_REAL sum, *bim, *akm;
+const int bsize=__BLOCKSZ__;
+
+#define __MIN__(x, y) (((x)<=(y))? (x) : (y))
+#define __MAX__(x, y) (((x)>=(y))? (x) : (y))
+
+ /* compute upper triangular part using blocking */
+ for(jj=0; jj<m; jj+=bsize){
+ for(i=0; i<m; ++i){
+ bim=b+i*m;
+ for(j=__MAX__(jj, i); j<__MIN__(jj+bsize, m); ++j)
+ bim[j]=0.0; //b[i*m+j]=0.0;
+ }
+
+ for(kk=0; kk<n; kk+=bsize){
+ for(i=0; i<m; ++i){
+ bim=b+i*m;
+ for(j=__MAX__(jj, i); j<__MIN__(jj+bsize, m); ++j){
+ sum=0.0;
+ for(k=kk; k<__MIN__(kk+bsize, n); ++k){
+ akm=a+k*m;
+ sum+=akm[i]*akm[j]; //a[k*m+i]*a[k*m+j];
+ }
+ bim[j]+=sum; //b[i*m+j]+=sum;
+ }
+ }
+ }
+ }
+
+ /* copy upper triangular part to the lower one */
+ for(i=0; i<m; ++i)
+ for(j=0; j<i; ++j)
+ b[i*m+j]=b[j*m+i];
+
+#undef __MIN__
+#undef __MAX__
+
+#endif /* HAVE_LAPACK */
+}
+
+/* forward finite difference approximation to the Jacobian of func */
+void LEVMAR_FDIF_FORW_JAC_APPROX(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ /* function to differentiate */
+ LM_REAL *p, /* I: current parameter estimate, mx1 */
+ LM_REAL *hx, /* I: func evaluated at p, i.e. hx=func(p), nx1 */
+ LM_REAL *hxx, /* W/O: work array for evaluating func(p+delta), nx1 */
+ LM_REAL delta, /* increment for computing the Jacobian */
+ LM_REAL *jac, /* O: array for storing approximated Jacobian, nxm */
+ int m,
+ int n,
+ void *adata)
+{
+register int i, j;
+LM_REAL tmp;
+register LM_REAL d;
+
+ for(j=0; j<m; ++j){
+ /* determine d=max(1E-04*|p[j]|, delta), see HZ */
+ d=LM_CNST(1E-04)*p[j]; // force evaluation
+ d=FABS(d);
+ if(d<delta)
+ d=delta;
+
+ tmp=p[j];
+ p[j]+=d;
+
+ (*func)(p, hxx, m, n, adata);
+
+ p[j]=tmp; /* restore */
+
+ d=LM_CNST(1.0)/d; /* invert so that divisions can be carried out faster as multiplications */
+ for(i=0; i<n; ++i){
+ jac[i*m+j]=(hxx[i]-hx[i])*d;
+ }
+ }
+}
+
+/* central finite difference approximation to the Jacobian of func */
+void LEVMAR_FDIF_CENT_JAC_APPROX(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ /* function to differentiate */
+ LM_REAL *p, /* I: current parameter estimate, mx1 */
+ LM_REAL *hxm, /* W/O: work array for evaluating func(p-delta), nx1 */
+ LM_REAL *hxp, /* W/O: work array for evaluating func(p+delta), nx1 */
+ LM_REAL delta, /* increment for computing the Jacobian */
+ LM_REAL *jac, /* O: array for storing approximated Jacobian, nxm */
+ int m,
+ int n,
+ void *adata)
+{
+register int i, j;
+LM_REAL tmp;
+register LM_REAL d;
+
+ for(j=0; j<m; ++j){
+ /* determine d=max(1E-04*|p[j]|, delta), see HZ */
+ d=LM_CNST(1E-04)*p[j]; // force evaluation
+ d=FABS(d);
+ if(d<delta)
+ d=delta;
+
+ tmp=p[j];
+ p[j]-=d;
+ (*func)(p, hxm, m, n, adata);
+
+ p[j]=tmp+d;
+ (*func)(p, hxp, m, n, adata);
+ p[j]=tmp; /* restore */
+
+ d=LM_CNST(0.5)/d; /* invert so that divisions can be carried out faster as multiplications */
+ for(i=0; i<n; ++i){
+ jac[i*m+j]=(hxp[i]-hxm[i])*d;
+ }
+ }
+}
+
+/*
+ * Check the Jacobian of a n-valued nonlinear function in m variables
+ * evaluated at a point p, for consistency with the function itself.
+ *
+ * Based on fortran77 subroutine CHKDER by
+ * Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
+ * Argonne National Laboratory. MINPACK project. March 1980.
+ *
+ *
+ * func points to a function from R^m --> R^n: Given a p in R^m it yields hx in R^n
+ * jacf points to a function implementing the Jacobian of func, whose correctness
+ * is to be tested. Given a p in R^m, jacf computes into the nxm matrix j the
+ * Jacobian of func at p. Note that row i of j corresponds to the gradient of
+ * the i-th component of func, evaluated at p.
+ * p is an input array of length m containing the point of evaluation.
+ * m is the number of variables
+ * n is the number of functions
+ * adata points to possible additional data and is passed uninterpreted
+ * to func, jacf.
+ * err is an array of length n. On output, err contains measures
+ * of correctness of the respective gradients. if there is
+ * no severe loss of significance, then if err[i] is 1.0 the
+ * i-th gradient is correct, while if err[i] is 0.0 the i-th
+ * gradient is incorrect. For values of err between 0.0 and 1.0,
+ * the categorization is less certain. In general, a value of
+ * err[i] greater than 0.5 indicates that the i-th gradient is
+ * probably correct, while a value of err[i] less than 0.5
+ * indicates that the i-th gradient is probably incorrect.
+ *
+ *
+ * The function does not perform reliably if cancellation or
+ * rounding errors cause a severe loss of significance in the
+ * evaluation of a function. therefore, none of the components
+ * of p should be unusually small (in particular, zero) or any
+ * other value which may cause loss of significance.
+ */
+
+void LEVMAR_CHKJAC(
+ void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata),
+ LM_REAL *p, int m, int n, void *adata, LM_REAL *err)
+{
+LM_REAL factor=LM_CNST(100.0);
+LM_REAL one=LM_CNST(1.0);
+LM_REAL zero=LM_CNST(0.0);
+LM_REAL *fvec, *fjac, *pp, *fvecp, *buf;
+
+register int i, j;
+LM_REAL eps, epsf, temp, epsmch;
+LM_REAL epslog;
+int fvec_sz=n, fjac_sz=n*m, pp_sz=m, fvecp_sz=n;
+
+ epsmch=LM_REAL_EPSILON;
+ eps=(LM_REAL)sqrt(epsmch);
+
+ buf=(LM_REAL *)malloc((fvec_sz + fjac_sz + pp_sz + fvecp_sz)*sizeof(LM_REAL));
+ if(!buf){
+ fprintf(stderr, LCAT(LEVMAR_CHKJAC, "(): memory allocation request failed\n"));
+ exit(1);
+ }
+ fvec=buf;
+ fjac=fvec+fvec_sz;
+ pp=fjac+fjac_sz;
+ fvecp=pp+pp_sz;
+
+ /* compute fvec=func(p) */
+ (*func)(p, fvec, m, n, adata);
+
+ /* compute the Jacobian at p */
+ (*jacf)(p, fjac, m, n, adata);
+
+ /* compute pp */
+ for(j=0; j<m; ++j){
+ temp=eps*FABS(p[j]);
+ if(temp==zero) temp=eps;
+ pp[j]=p[j]+temp;
+ }
+
+ /* compute fvecp=func(pp) */
+ (*func)(pp, fvecp, m, n, adata);
+
+ epsf=factor*epsmch;
+ epslog=(LM_REAL)log10(eps);
+
+ for(i=0; i<n; ++i)
+ err[i]=zero;
+
+ for(j=0; j<m; ++j){
+ temp=FABS(p[j]);
+ if(temp==zero) temp=one;
+
+ for(i=0; i<n; ++i)
+ err[i]+=temp*fjac[i*m+j];
+ }
+
+ for(i=0; i<n; ++i){
+ temp=one;
+ if(fvec[i]!=zero && fvecp[i]!=zero && FABS(fvecp[i]-fvec[i])>=epsf*FABS(fvec[i]))
+ temp=eps*FABS((fvecp[i]-fvec[i])/eps - err[i])/(FABS(fvec[i])+FABS(fvecp[i]));
+ err[i]=one;
+ if(temp>epsmch && temp<eps)
+ err[i]=((LM_REAL)log10(temp) - epslog)/epslog;
+ if(temp>=eps) err[i]=zero;
+ }
+
+ free(buf);
+
+ return;
+}
+
+#ifdef HAVE_LAPACK
+/*
+ * This function computes the pseudoinverse of a square matrix A
+ * into B using SVD. A and B can coincide
+ *
+ * The function returns 0 in case of error (e.g. A is singular),
+ * the rank of A if successful
+ *
+ * A, B are mxm
+ *
+ */
+static int LEVMAR_PSEUDOINVERSE(LM_REAL *A, LM_REAL *B, int m)
+{
+LM_REAL *buf=NULL;
+int buf_sz=0;
+static LM_REAL eps=LM_CNST(-1.0);
+
+register int i, j;
+LM_REAL *a, *u, *s, *vt, *work;
+int a_sz, u_sz, s_sz, vt_sz, tot_sz;
+LM_REAL thresh, one_over_denom;
+int info, rank, worksz, *iwork, iworksz;
+
+ /* calculate required memory size */
+ worksz=5*m; // min worksize for GESVD
+ //worksz=m*(7*m+4); // min worksize for GESDD
+ iworksz=8*m;
+ a_sz=m*m;
+ u_sz=m*m; s_sz=m; vt_sz=m*m;
+
+ tot_sz=(a_sz + u_sz + s_sz + vt_sz + worksz)*sizeof(LM_REAL) + iworksz*sizeof(int); /* should be arranged in that order for proper doubles alignment */
+
+ buf_sz=tot_sz;
+ buf=(LM_REAL *)malloc(buf_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", LEVMAR_PSEUDOINVERSE) "() failed!\n");
+ return 0; /* error */
+ }
+
+ a=buf;
+ u=a+a_sz;
+ s=u+u_sz;
+ vt=s+s_sz;
+ work=vt+vt_sz;
+ iwork=(int *)(work+worksz);
+
+ /* store A (column major!) into a */
+ for(i=0; i<m; i++)
+ for(j=0; j<m; j++)
+ a[i+j*m]=A[i*m+j];
+
+ /* SVD decomposition of A */
+ GESVD("A", "A", (int *)&m, (int *)&m, a, (int *)&m, s, u, (int *)&m, vt, (int *)&m, work, (int *)&worksz, &info);
+ //GESDD("A", (int *)&m, (int *)&m, a, (int *)&m, s, u, (int *)&m, vt, (int *)&m, work, (int *)&worksz, iwork, &info);
+
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, RCAT(RCAT(RCAT("LAPACK error: illegal value for argument %d of ", GESVD), "/" GESDD) " in ", LEVMAR_PSEUDOINVERSE) "()\n", -info);
+ }
+ else{
+ fprintf(stderr, RCAT("LAPACK error: dgesdd (dbdsdc)/dgesvd (dbdsqr) failed to converge in ", LEVMAR_PSEUDOINVERSE) "() [info=%d]\n", info);
+ }
+ free(buf);
+ return 0;
+ }
+
+ if(eps<0.0){
+ LM_REAL aux;
+
+ /* compute machine epsilon */
+ for(eps=LM_CNST(1.0); aux=eps+LM_CNST(1.0), aux-LM_CNST(1.0)>0.0; eps*=LM_CNST(0.5))
+ ;
+ eps*=LM_CNST(2.0);
+ }
+
+ /* compute the pseudoinverse in B */
+ for(i=0; i<a_sz; i++) B[i]=0.0; /* initialize to zero */
+ for(rank=0, thresh=eps*s[0]; rank<m && s[rank]>thresh; rank++){
+ one_over_denom=LM_CNST(1.0)/s[rank];
+
+ for(j=0; j<m; j++)
+ for(i=0; i<m; i++)
+ B[i*m+j]+=vt[rank+i*m]*u[j+rank*m]*one_over_denom;
+ }
+
+ free(buf);
+
+ return rank;
+}
+#else // no LAPACK
+
+/*
+ * This function computes the inverse of A in B. A and B can coincide
+ *
+ * The function employs LAPACK-free LU decomposition of A to solve m linear
+ * systems A*B_i=I_i, where B_i and I_i are the i-th columns of B and I.
+ *
+ * A and B are mxm
+ *
+ * The function returns 0 in case of error, 1 if successful
+ *
+ */
+static int LEVMAR_LUINVERSE(LM_REAL *A, LM_REAL *B, int m)
+{
+void *buf=NULL;
+int buf_sz=0;
+
+register int i, j, k, l;
+int *idx, maxi=-1, idx_sz, a_sz, x_sz, work_sz, tot_sz;
+LM_REAL *a, *x, *work, max, sum, tmp;
+
+ /* calculate required memory size */
+ idx_sz=m;
+ a_sz=m*m;
+ x_sz=m;
+ work_sz=m;
+ tot_sz=(a_sz + x_sz + work_sz)*sizeof(LM_REAL) + idx_sz*sizeof(int); /* should be arranged in that order for proper doubles alignment */
+
+ buf_sz=tot_sz;
+ buf=(void *)malloc(tot_sz);
+ if(!buf){
+ fprintf(stderr, RCAT("memory allocation in ", LEVMAR_LUINVERSE) "() failed!\n");
+ return 0; /* error */
+ }
+
+ a=buf;
+ x=a+a_sz;
+ work=x+x_sz;
+ idx=(int *)(work+work_sz);
+
+ /* avoid destroying A by copying it to a */
+ for(i=0; i<a_sz; ++i) a[i]=A[i];
+
+ /* compute the LU decomposition of a row permutation of matrix a; the permutation itself is saved in idx[] */
+ for(i=0; i<m; ++i){
+ max=0.0;
+ for(j=0; j<m; ++j)
+ if((tmp=FABS(a[i*m+j]))>max)
+ max=tmp;
+ if(max==0.0){
+ fprintf(stderr, RCAT("Singular matrix A in ", LEVMAR_LUINVERSE) "()!\n");
+ free(buf);
+
+ return 0;
+ }
+ work[i]=LM_CNST(1.0)/max;
+ }
+
+ for(j=0; j<m; ++j){
+ for(i=0; i<j; ++i){
+ sum=a[i*m+j];
+ for(k=0; k<i; ++k)
+ sum-=a[i*m+k]*a[k*m+j];
+ a[i*m+j]=sum;
+ }
+ max=0.0;
+ for(i=j; i<m; ++i){
+ sum=a[i*m+j];
+ for(k=0; k<j; ++k)
+ sum-=a[i*m+k]*a[k*m+j];
+ a[i*m+j]=sum;
+ if((tmp=work[i]*FABS(sum))>=max){
+ max=tmp;
+ maxi=i;
+ }
+ }
+ if(j!=maxi){
+ for(k=0; k<m; ++k){
+ tmp=a[maxi*m+k];
+ a[maxi*m+k]=a[j*m+k];
+ a[j*m+k]=tmp;
+ }
+ work[maxi]=work[j];
+ }
+ idx[j]=maxi;
+ if(a[j*m+j]==0.0)
+ a[j*m+j]=LM_REAL_EPSILON;
+ if(j!=m-1){
+ tmp=LM_CNST(1.0)/(a[j*m+j]);
+ for(i=j+1; i<m; ++i)
+ a[i*m+j]*=tmp;
+ }
+ }
+
+ /* The decomposition has now replaced a. Solve the m linear systems using
+ * forward and back substitution
+ */
+ for(l=0; l<m; ++l){
+ for(i=0; i<m; ++i) x[i]=0.0;
+ x[l]=LM_CNST(1.0);
+
+ for(i=k=0; i<m; ++i){
+ j=idx[i];
+ sum=x[j];
+ x[j]=x[i];
+ if(k!=0)
+ for(j=k-1; j<i; ++j)
+ sum-=a[i*m+j]*x[j];
+ else
+ if(sum!=0.0)
+ k=i+1;
+ x[i]=sum;
+ }
+
+ for(i=m-1; i>=0; --i){
+ sum=x[i];
+ for(j=i+1; j<m; ++j)
+ sum-=a[i*m+j]*x[j];
+ x[i]=sum/a[i*m+i];
+ }
+
+ for(i=0; i<m; ++i)
+ B[i*m+l]=x[i];
+ }
+
+ free(buf);
+
+ return 1;
+}
+#endif /* HAVE_LAPACK */
+
+/*
+ * This function computes in C the covariance matrix corresponding to a least
+ * squares fit. JtJ is the approximate Hessian at the solution (i.e. J^T*J, where
+ * J is the Jacobian at the solution), sumsq is the sum of squared residuals
+ * (i.e. goodnes of fit) at the solution, m is the number of parameters (variables)
+ * and n the number of observations. JtJ can coincide with C.
+ *
+ * if JtJ is of full rank, C is computed as sumsq/(n-m)*(JtJ)^-1
+ * otherwise and if LAPACK is available, C=sumsq/(n-r)*(JtJ)^+
+ * where r is JtJ's rank and ^+ denotes the pseudoinverse
+ * The diagonal of C is made up from the estimates of the variances
+ * of the estimated regression coefficients.
+ * See the documentation of routine E04YCF from the NAG fortran lib
+ *
+ * The function returns the rank of JtJ if successful, 0 on error
+ *
+ * A and C are mxm
+ *
+ */
+int LEVMAR_COVAR(LM_REAL *JtJ, LM_REAL *C, LM_REAL sumsq, int m, int n)
+{
+register int i;
+int rnk;
+LM_REAL fact;
+
+#ifdef HAVE_LAPACK
+ rnk=LEVMAR_PSEUDOINVERSE(JtJ, C, m);
+ if(!rnk) return 0;
+#else
+#ifdef _MSC_VER
+#pragma message("LAPACK not available, LU will be used for matrix inversion when computing the covariance; this might be unstable at times")
+#else
+#warning LAPACK not available, LU will be used for matrix inversion when computing the covariance; this might be unstable at times
+#endif // _MSC_VER
+
+ rnk=LEVMAR_LUINVERSE(JtJ, C, m);
+ if(!rnk) return 0;
+
+ rnk=m; /* assume full rank */
+#endif /* HAVE_LAPACK */
+
+ fact=sumsq/(LM_REAL)(n-rnk);
+ for(i=0; i<m*m; ++i)
+ C[i]*=fact;
+
+ return rnk;
+}
+
+/* standard deviation of the best-fit parameter i.
+ * covar is the mxm covariance matrix of the best-fit parameters (see also LEVMAR_COVAR()).
+ *
+ * The standard deviation is computed as \sigma_{i} = \sqrt{C_{ii}}
+ */
+LM_REAL LEVMAR_STDDEV(LM_REAL *covar, int m, int i)
+{
+ return (LM_REAL)sqrt(covar[i*m+i]);
+}
+
+/* Pearson's correlation coefficient of the best-fit parameters i and j.
+ * covar is the mxm covariance matrix of the best-fit parameters (see also LEVMAR_COVAR()).
+ *
+ * The coefficient is computed as \rho_{ij} = C_{ij} / sqrt(C_{ii} C_{jj})
+ */
+LM_REAL LEVMAR_CORCOEF(LM_REAL *covar, int m, int i, int j)
+{
+ return (LM_REAL)(covar[i*m+j]/sqrt(covar[i*m+i]*covar[j*m+j]));
+}
+
+/* coefficient of determination.
+ * see http://en.wikipedia.org/wiki/Coefficient_of_determination
+ */
+LM_REAL LEVMAR_R2(void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata),
+ LM_REAL *p, LM_REAL *x, int m, int n, void *adata)
+{
+register int i;
+register LM_REAL tmp;
+LM_REAL SSerr, // sum of squared errors, i.e. residual sum of squares \sum_i (x_i-hx_i)^2
+ SStot, // \sum_i (x_i-xavg)^2
+ *hx, xavg;
+
+
+ if((hx=(LM_REAL *)malloc(n*sizeof(LM_REAL)))==NULL){
+ fprintf(stderr, RCAT("memory allocation request failed in ", LEVMAR_R2) "()\n");
+ exit(1);
+ }
+
+ /* hx=f(p) */
+ (*func)(p, hx, m, n, adata);
+
+ for(i=0, tmp=0.0; i<n; ++i)
+ tmp+=x[i];
+ xavg=tmp/(LM_REAL)n;
+
+ for(i=0, SSerr=SStot=0.0; i<n; ++i){
+ tmp=x[i]-hx[i];
+ SSerr+=tmp*tmp;
+
+ tmp=x[i]-xavg;
+ SStot+=tmp*tmp;
+ }
+
+ free(hx);
+
+ return LM_CNST(1.0) - SSerr/SStot;
+}
+
+/* check box constraints for consistency */
+int LEVMAR_BOX_CHECK(LM_REAL *lb, LM_REAL *ub, int m)
+{
+register int i;
+
+ if(!lb || !ub) return 1;
+
+ for(i=0; i<m; ++i)
+ if(lb[i]>ub[i]) return 0;
+
+ return 1;
+}
+
+#ifdef HAVE_LAPACK
+
+/* compute the Cholesky decomposition of C in W, s.t. C=W^t W and W is upper triangular */
+int LEVMAR_CHOLESKY(LM_REAL *C, LM_REAL *W, int m)
+{
+register int i, j;
+int info;
+
+ /* copy weights array C to W so that LAPACK won't destroy it;
+ * C is assumed symmetric, hence no transposition is needed
+ */
+ for(i=0, j=m*m; i<j; ++i)
+ W[i]=C[i];
+
+ /* Cholesky decomposition */
+ POTF2("U", (int *)&m, W, (int *)&m, (int *)&info);
+ /* error treatment */
+ if(info!=0){
+ if(info<0){
+ fprintf(stderr, "LAPACK error: illegal value for argument %d of dpotf2 in %s\n", -info, LCAT(LEVMAR_CHOLESKY, "()"));
+ }
+ else{
+ fprintf(stderr, "LAPACK error: the leading minor of order %d is not positive definite,\n%s()\n", info,
+ RCAT("and the Cholesky factorization could not be completed in ", LEVMAR_CHOLESKY));
+ }
+ return LM_ERROR;
+ }
+
+ /* the decomposition is in the upper part of W (in column-major order!).
+ * copying it to the lower part and zeroing the upper transposes
+ * W in row-major order
+ */
+ for(i=0; i<m; i++)
+ for(j=0; j<i; j++){
+ W[i+j*m]=W[j+i*m];
+ W[j+i*m]=0.0;
+ }
+
+ return 0;
+}
+#endif /* HAVE_LAPACK */
+
+
+/* Compute e=x-y for two n-vectors x and y and return the squared L2 norm of e.
+ * e can coincide with either x or y; x can be NULL, in which case it is assumed
+ * to be equal to the zero vector.
+ * Uses loop unrolling and blocking to reduce bookkeeping overhead & pipeline
+ * stalls and increase instruction-level parallelism; see http://www.abarnett.demon.co.uk/tutorial.html
+ */
+
+LM_REAL LEVMAR_L2NRMXMY(LM_REAL *e, LM_REAL *x, LM_REAL *y, int n)
+{
+const int blocksize=8, bpwr=3; /* 8=2^3 */
+register int i;
+int j1, j2, j3, j4, j5, j6, j7;
+int blockn;
+register LM_REAL sum0=0.0, sum1=0.0, sum2=0.0, sum3=0.0;
+
+ /* n may not be divisible by blocksize,
+ * go as near as we can first, then tidy up.
+ */
+ blockn = (n>>bpwr)<<bpwr; /* (n / blocksize) * blocksize; */
+
+ /* unroll the loop in blocks of `blocksize'; looping downwards gains some more speed */
+ if(x){
+ for(i=blockn-1; i>0; i-=blocksize){
+ e[i ]=x[i ]-y[i ]; sum0+=e[i ]*e[i ];
+ j1=i-1; e[j1]=x[j1]-y[j1]; sum1+=e[j1]*e[j1];
+ j2=i-2; e[j2]=x[j2]-y[j2]; sum2+=e[j2]*e[j2];
+ j3=i-3; e[j3]=x[j3]-y[j3]; sum3+=e[j3]*e[j3];
+ j4=i-4; e[j4]=x[j4]-y[j4]; sum0+=e[j4]*e[j4];
+ j5=i-5; e[j5]=x[j5]-y[j5]; sum1+=e[j5]*e[j5];
+ j6=i-6; e[j6]=x[j6]-y[j6]; sum2+=e[j6]*e[j6];
+ j7=i-7; e[j7]=x[j7]-y[j7]; sum3+=e[j7]*e[j7];
+ }
+
+ /*
+ * There may be some left to do.
+ * This could be done as a simple for() loop,
+ * but a switch is faster (and more interesting)
+ */
+
+ i=blockn;
+ if(i<n){
+ /* Jump into the case at the place that will allow
+ * us to finish off the appropriate number of items.
+ */
+
+ switch(n - i){
+ case 7 : e[i]=x[i]-y[i]; sum0+=e[i]*e[i]; ++i;
+ case 6 : e[i]=x[i]-y[i]; sum1+=e[i]*e[i]; ++i;
+ case 5 : e[i]=x[i]-y[i]; sum2+=e[i]*e[i]; ++i;
+ case 4 : e[i]=x[i]-y[i]; sum3+=e[i]*e[i]; ++i;
+ case 3 : e[i]=x[i]-y[i]; sum0+=e[i]*e[i]; ++i;
+ case 2 : e[i]=x[i]-y[i]; sum1+=e[i]*e[i]; ++i;
+ case 1 : e[i]=x[i]-y[i]; sum2+=e[i]*e[i]; //++i;
+ }
+ }
+ }
+ else{ /* x==0 */
+ for(i=blockn-1; i>0; i-=blocksize){
+ e[i ]=-y[i ]; sum0+=e[i ]*e[i ];
+ j1=i-1; e[j1]=-y[j1]; sum1+=e[j1]*e[j1];
+ j2=i-2; e[j2]=-y[j2]; sum2+=e[j2]*e[j2];
+ j3=i-3; e[j3]=-y[j3]; sum3+=e[j3]*e[j3];
+ j4=i-4; e[j4]=-y[j4]; sum0+=e[j4]*e[j4];
+ j5=i-5; e[j5]=-y[j5]; sum1+=e[j5]*e[j5];
+ j6=i-6; e[j6]=-y[j6]; sum2+=e[j6]*e[j6];
+ j7=i-7; e[j7]=-y[j7]; sum3+=e[j7]*e[j7];
+ }
+
+ /*
+ * There may be some left to do.
+ * This could be done as a simple for() loop,
+ * but a switch is faster (and more interesting)
+ */
+
+ i=blockn;
+ if(i<n){
+ /* Jump into the case at the place that will allow
+ * us to finish off the appropriate number of items.
+ */
+
+ switch(n - i){
+ case 7 : e[i]=-y[i]; sum0+=e[i]*e[i]; ++i;
+ case 6 : e[i]=-y[i]; sum1+=e[i]*e[i]; ++i;
+ case 5 : e[i]=-y[i]; sum2+=e[i]*e[i]; ++i;
+ case 4 : e[i]=-y[i]; sum3+=e[i]*e[i]; ++i;
+ case 3 : e[i]=-y[i]; sum0+=e[i]*e[i]; ++i;
+ case 2 : e[i]=-y[i]; sum1+=e[i]*e[i]; ++i;
+ case 1 : e[i]=-y[i]; sum2+=e[i]*e[i]; //++i;
+ }
+ }
+ }
+
+ return sum0+sum1+sum2+sum3;
+}
+
+/* undefine everything. THIS MUST REMAIN AT THE END OF THE FILE */
+#undef LEVMAR_PSEUDOINVERSE
+#undef LEVMAR_LUINVERSE
+#undef LEVMAR_BOX_CHECK
+#undef LEVMAR_CHOLESKY
+#undef LEVMAR_COVAR
+#undef LEVMAR_STDDEV
+#undef LEVMAR_CORCOEF
+#undef LEVMAR_R2
+#undef LEVMAR_CHKJAC
+#undef LEVMAR_FDIF_FORW_JAC_APPROX
+#undef LEVMAR_FDIF_CENT_JAC_APPROX
+#undef LEVMAR_TRANS_MAT_MAT_MULT
+#undef LEVMAR_L2NRMXMY
diff --git a/sci-libs/lpsolve/Manifest b/sci-libs/lpsolve/Manifest
new file mode 100644
index 000000000..aae2cd6e8
--- /dev/null
+++ b/sci-libs/lpsolve/Manifest
@@ -0,0 +1,2 @@
+DIST lp_solve_5.5.0.15_source.tar.gz 802881 RMD160 2a7d43c3c0627a49e03f9e1924932f0f0441e011 SHA1 e4b80684e8c1fe996b6bc90c01ce5cf022d9ccfe SHA256 ea1243e8aa2f0d52172dc0a90d1c2a8d2a4f696a39fc9cf07321810363d18985
+EBUILD lpsolve-5.5.0.15.ebuild 585 RMD160 3459ae31248a518a9c4caf022d17bc20abec7130 SHA1 dc6a3c0bd49f7d5bc6dd1bce534c3ec332eeed43 SHA256 6615a64fc57813d1a6141a21247318997df9b53e38a20c9f001579328805b62b
diff --git a/sci-libs/lpsolve/lpsolve-5.5.0.15.ebuild b/sci-libs/lpsolve/lpsolve-5.5.0.15.ebuild
new file mode 100644
index 000000000..0d0ad5f61
--- /dev/null
+++ b/sci-libs/lpsolve/lpsolve-5.5.0.15.ebuild
@@ -0,0 +1,53 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+EAPI=3
+
+inherit eutils toolchain-funcs
+
+MYPN="lp_solve"
+DESCRIPTION="Library for solving (mixed integer) linear programming problems"
+HOMEPAGE="http://lpsolve.sourceforge.net/"
+SRC_URI="mirror://sourceforge/${PN}/${PN}/${PV}/${MYPN}_${PV}_source.tar.gz"
+
+LICENSE="LPGL-2.1"
+SLOT="0"
+KEYWORDS="~amd64 ~x86"
+
+IUSE="static-libs examples"
+DEPEND=""
+RDEPEND="${DEPEND}"
+
+S="${WORKDIR}/${MYPN}_${PV}"
+
+PLIB=lpsolve55
+
+src_prepare() {
+ LPSOLVE_ARCH=ux32
+ LPSOLVE_ARCH=ux64
+ sed -i \
+ "s|^c=.*$$|c=$(tc-getCC)|g" \
+ 's|^opts=.*$$|opts="${CFLAGS}"|g' \
+ "s|-fpic|-fPIC|g" \
+ "s|-ldl||g" \
+ lp_solve/ccc ${PLIB}/ccc || die "sed failed"
+
+src_compile() {
+ for d in lp_solve ${PLIB}; do
+ pushd $d &> /dev/null
+ bash -x ccc
+ popd &> /dev/null
+ done
+}
+
+src_install() {
+ dobin lp_solve/bin/${LPSOLVE_ARCH}/lp_solve || die
+ insinto /usr/include/lpsolve
+ doins declare.h fortify.h ini.h lp_*.h lpkit.h lpsolve.h ufortify.h yacc_read.h
+ dolib.so lib${PLIB}.so
+ if use static-libs; then
+ dolib.a lib${PLIB}.a || die
+ fi
+ dosym lib${PLIB}.so /usr/$(get_libdir)/lib${PLIB}.so.1
+}
diff --git a/sci-libs/pagmo/Manifest b/sci-libs/pagmo/Manifest
new file mode 100644
index 000000000..0a62ed106
--- /dev/null
+++ b/sci-libs/pagmo/Manifest
@@ -0,0 +1 @@
+EBUILD pagmo-9999.ebuild 887 RMD160 4c9f301299d663468dfc64b13fa76c1db1c559c3 SHA1 2c608caa9a20559de1c53fb7d5bd015707e3f443 SHA256 f836edbbdaaf3072b8a0b3e5247b2f02c1be4564425a379ead3804ff9643e841
diff --git a/sci-libs/pagmo/pagmo-9999.ebuild b/sci-libs/pagmo/pagmo-9999.ebuild
new file mode 100644
index 000000000..8a898fc1e
--- /dev/null
+++ b/sci-libs/pagmo/pagmo-9999.ebuild
@@ -0,0 +1,35 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+EAPI="2"
+inherit cmake-utils git
+
+DESCRIPTION="Parallelization engine for optimization problems"
+HOMEPAGE="http://pagmo.sourceforge.net/"
+EGIT_REPO_URI="git://pagmo.git.sourceforge.net/gitroot/pagmo/pagmo"
+SRC_URI=""
+
+LICENSE="GPL-3"
+SLOT="0"
+IUSE="gsl kepler mpi nlopt python test"
+KEYWORDS="~amd64 ~x86"
+
+RDEPEND="dev-libs/boost[mpi?,python?]
+ gsl? ( sci-libs/gsl )
+ nlopt? ( sci-libs/nlopt )"
+DEPEND="${RDEPEND}"
+
+src_configure() {
+ mycmakeargs=(
+ -DENABLE_SNOPT=OFF
+ -DBUILD_MAIN=OFF
+ $(cmake-utils_use_build python PYGMO)
+ $(cmake-utils_use_enable gsl GSL)
+ $(cmake-utils_use_enable kepler KEPLERIAN_TOOLBOX)
+ $(cmake-utils_use_enable mpi MPI)
+ $(cmake-utils_use_enable nlopt NLOPT)
+ $(cmake-utils_use_enable test TESTS)
+ )
+ cmake-utils_src_configure
+}
diff --git a/sci-libs/shogun/Manifest b/sci-libs/shogun/Manifest
new file mode 100644
index 000000000..a8c260cf2
--- /dev/null
+++ b/sci-libs/shogun/Manifest
@@ -0,0 +1,4 @@
+AUX shogun-0.9.3-lapack.patch 583 RMD160 e1e70f045fa448601e8f43b6a7603030981dbf97 SHA1 abef54b23c9041ef65faa7e0324e89fed52749c4 SHA256 b86cbcb4f0003754393c6e55f501b0721d3479ee6bed48c876f1ee564b9a2825
+DIST shogun-0.9.3.tar.bz2 2853271 RMD160 9638a6b747a1177b048720b8999c60f33c7df5ef SHA1 d559dff3e11f777a23f00278d78d259ad896b829 SHA256 597d08155c7eff894dfae64dff8d4b37a5aba1da4a87b335881bddcb1a587526
+EBUILD shogun-0.9.3.ebuild 1625 RMD160 79dfc02d159c84b6cfcd749150779979e8273cdc SHA1 17df8f64dbe4270ead4158f54a6928fa05095a76 SHA256 42e0e74ebf89cff12b1c461264067ccf32350abd63899404add950aa8d290968
+MISC metadata.xml 1297 RMD160 dcc89a39c7d7a60ae5c128b8778cd314646e1417 SHA1 9ffe75693797b1eeb466223d6332cd589b32c624 SHA256 84677e9d55cab8bed4c8d99fdb917a6a31d21904526f20b95137716e6fed63e0
diff --git a/sci-libs/shogun/files/shogun-0.9.3-lapack.patch b/sci-libs/shogun/files/shogun-0.9.3-lapack.patch
new file mode 100644
index 000000000..32e41aa1d
--- /dev/null
+++ b/sci-libs/shogun/files/shogun-0.9.3-lapack.patch
@@ -0,0 +1,18 @@
+--- src/configure.orig 2010-06-01 19:49:30.000000000 +0100
++++ src/configure 2010-06-01 19:52:05.000000000 +0100
+@@ -2421,13 +2421,13 @@
+ }
+ EOF
+ echocheck "AMD ACML support"
+- if cc_check -lacml -lcblas -lgfortran
++ if cc_check $(pkg-config --libs cblas lapack)
+ then
+ echores "yes"
+ HAVE_ACML='#define HAVE_ACML 1'
+ HAVE_LAPACK='#define HAVE_LAPACK 1'
+ DEFINES="$DEFINES -DHAVE_ACML -DHAVE_LAPACK"
+- LINKFLAGS="$LINKFLAGS -lacml -lcblas -lgfortran"
++ LINKFLAGS="$LINKFLAGS $(pkg-config --libs cblas lapack)"
+ else
+ echores "no"
+
diff --git a/sci-libs/shogun/metadata.xml b/sci-libs/shogun/metadata.xml
new file mode 100644
index 000000000..b5cdff054
--- /dev/null
+++ b/sci-libs/shogun/metadata.xml
@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+<herd>sci</herd>
+<longdescription>
+ SHOGUN is a new machine learning toolbox with focus on large scale kernel
+ methods and especially on Support Vector Machines (SVM) with focus to
+ bioinformatics. It provides a generic SVM object interfacing to several
+ different SVM implementations. Each of the SVMs can be combined with a variety
+ of the many kernels implemented. It can deal with weighted linear combination
+ of a number of sub-kernels, each of which not necessarily working on the same
+ domain, where an optimal sub-kernel weighting can be learned using Multiple
+ Kernel Learning. Apart from SVM 2-class classification and regression
+ problems, a number of linear methods like Linear Discriminant Analysis (LDA),
+ Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to
+ train hidden markov models are implemented. The input feature-objects can be
+ dense, sparse or strings and of type int/short/double/char and can be
+ converted into different feature types. Chains of preprocessors (e.g.
+ substracting the mean) can be attached to each feature object allowing for
+ on-the-fly pre-processing.
+</longdescription>
+</pkgmetadata>
diff --git a/sci-libs/shogun/shogun-0.9.3.ebuild b/sci-libs/shogun/shogun-0.9.3.ebuild
new file mode 100644
index 000000000..321959d93
--- /dev/null
+++ b/sci-libs/shogun/shogun-0.9.3.ebuild
@@ -0,0 +1,64 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+EAPI=2
+
+inherit eutils toolchain-funcs
+
+DESCRIPTION="Large Scale Machine Learning Toolbox"
+HOMEPAGE="http://www.shogun-toolbox.org/"
+SRC_URI="http://shogun-toolbox.org/archives/shogun/releases/${PV:0:3}/sources/${P}.tar.bz2"
+
+LICENSE="GPL-3"
+SLOT="0"
+KEYWORDS="~amd64 ~x86"
+
+IUSE="boost bz2 cplex doc glpk gzip hdf5 lapack lpsolve lzma lzo octave python R readline threads"
+
+RDEPEND="virtual/lapack
+ bzip2? ( app-arch/bzip2 )
+ cplex? ( sci-mathematics/cplex-bin )
+ glpk? ( sci-mathematics/glpk )
+ gzip? ( app-arch/gzip )
+ hdf5? ( sci-libs/hdf5 )
+ glpk? ( sci-mathematics/lpsolve )
+ lzma? ( app-arch/xz-utils )
+ lzo? ( dev-libs/lzo )
+ octave? ( sci-mathematics/octave )
+ python? ( dev-python/numpy )
+ R? ( dev-lang/R )
+ readline? ( sys-libs/readline )"
+
+DEPEND="${RDEPEND}
+ dev-libs/boost
+ dev-util/pkgconfig
+ doc? ( app-doc/doxygen )"
+
+S="${WORKDIR}/${P}/src"
+
+src_configure() {
+ # not an autotools configure (based on mplayer one)
+ # disable svmlight based on debian comment
+ ./configure \
+ --cc=$(tc-getCC) \
+ --cxx=$(tc-getCXX) \
+ --prefix=/usr \
+ --datadir=/usr/share/${PN} \
+ --mandir=/usr/share/man \
+ --confdir=/etc \
+ --libdir=/usr/$(get_libdir) \
+ --disable-cpudetection \
+ --disable-svm-light \
+ $(use_enable doc doxygen) \
+ $(use_enable boost boost-serialization) \
+ $(use_enable glpk) \
+ $(use_enable hdf5) \
+ $(use_enable lapack) \
+ $(use_enable readline) \
+ $(use_enable threads hmm-parallel)
+}
+
+src_install() {
+ emake DESTDIR="${D}" install || die "emake install failed"
+}
diff --git a/sci-physics/fedora b/sci-physics/fedora
new file mode 160000
+Subproject c60383e16ea43ddc971ddd92317094f6f984979
diff --git a/sci-physics/healpix/Manifest b/sci-physics/healpix/Manifest
new file mode 100644
index 000000000..f0584cc9f
--- /dev/null
+++ b/sci-physics/healpix/Manifest
@@ -0,0 +1,3 @@
+DIST Healpix_2.15a_2010Jun18.tar.gz 28853992 RMD160 2961593e2f6f3a20e2120a52952db5f8698005ae SHA1 9ca4b56775b20350cdf9067a4a6b885caa643724 SHA256 9123239171c70ca0f021367ebb0ba2a6581de4251729d6d3e9b0edc3f4e500c9
+EBUILD healpix-2.15a.ebuild 715 RMD160 c69125bdd304a208b82341506527d7bd1b6cf02f SHA1 e8feefb1db1f28585fd9db72be820757229ad63d SHA256 36fe133f3af3789cb471b018cf2bdd1420b4512f9dcaa41495f60add614f971f
+MISC metadata.xml 1238 RMD160 c82938e77f387291d6a1c3bf9b0d802f2878dd12 SHA1 b5738f14922c69e78cfb867acdc65eeea25977bf SHA256 2c13f98be5fa8bdaf21ac86bcd00bbeb8944df5615de0cfce35ae4c00ef4b4d5
diff --git a/sci-physics/healpix/healpix-2.15a.ebuild b/sci-physics/healpix/healpix-2.15a.ebuild
new file mode 100644
index 000000000..8e043ce29
--- /dev/null
+++ b/sci-physics/healpix/healpix-2.15a.ebuild
@@ -0,0 +1,33 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: $
+
+EAPI=2
+MYP="Healpix_${PV}"
+MYPP="2010Jun18"
+
+DESCRIPTION="Hierarchical Equal Area isoLatitude Pixelization of a sphere."
+HOMEPAGE="http://healpix.jpl.nasa.gov/index.shtml"
+SRC_URI="mirror://sourceforge/${PN}/${MYP}/${MYP}_${MYPP}.tar.gz"
+
+LICENSE="GPL-2"
+SLOT="0"
+KEYWORDS="~amd86 ~x86"
+
+IUSE="fortran idl java"
+DEPEND=">=sci-libs/cfitsio-3
+ fortran? ( media-libs/gd )"
+RDEPEND="${DEPEND}"
+
+S="${WORKDIR}/${MYP}"
+
+src_configure() {
+ econf \
+ $(use_enable fortran) \
+ $(use_enable idl) \
+ $(use_enable java)
+}
+
+src_install() {
+ emake DESTDIR="${D}" install || die "emake install failed"
+}
diff --git a/sci-physics/healpix/metadata.xml b/sci-physics/healpix/metadata.xml
new file mode 100644
index 000000000..741a15b6a
--- /dev/null
+++ b/sci-physics/healpix/metadata.xml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+<herd>sci-physics</herd>
+<longdescription lang='en'>
+XXXXX
+</longdescription>
+<use>
+ <flag name='blah'>Installs blah</flag>
+</use>
+</pkgmetadata>
diff --git a/sci-physics/root-9999.ebuild b/sci-physics/root-9999.ebuild
new file mode 100644
index 000000000..719702352
--- /dev/null
+++ b/sci-physics/root-9999.ebuild
@@ -0,0 +1,18 @@
+# Copyright 1999-2010 Gentoo Foundation
+# Distributed under the terms of the GNU General Public License v2
+# $Header: /var/cvsroot/gentoo-x86/sci-physics/root/root-5.26.00-r4.ebuild,v 1.4 2010/07/06 15:59:37 bicatali Exp $
+
+EAPI=3
+
+PYTHON_DEPEND="python? 2"
+ESVN_REPO_URI="https://root.cern.ch/svn/root/trunk/"
+
+inherit versionator eutils qt4 subversion elisp-common fdo-mime python toolchain-funcs
+
+DOC_PV=$(get_major_version)_$(get_version_component_range 2)
+ROOFIT_DOC_PV=2.91-33
+TMVA_DOC_PV=4
+
+DESCRIPTION="C++ data analysis framework and interpreter from CERN"
+HOMEPAGE="http://root.cern.ch/"
+SRC_URI=""