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##
## liblbfgs.spec -- OpenPKG RPM Package Specification
## Copyright (c) 2000-2022 OpenPKG Project <http://openpkg.org/>
##
## Permission to use, copy, modify, and distribute this software for
## any purpose with or without fee is hereby granted, provided that
## the above copyright notice and this permission notice appear in all
## copies.
##
## THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
## WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
## MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
## IN NO EVENT SHALL THE AUTHORS AND COPYRIGHT HOLDERS AND THEIR
## CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
## USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
## ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
## OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
## OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
## SUCH DAMAGE.
##
# package information
Name: liblbfgs
Summary: Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) Library
URL: http://www.chokkan.org/software/liblbfgs/
Vendor: Naoaki Okazaki
Packager: OpenPKG Project
Distribution: OpenPKG Community
Class: EVAL
Group: Algorithm
License: MIT
Version: 1.10
Release: 20180315
# list of sources
Source0: https://github.com/downloads/chokkan/liblbfgs/liblbfgs-%{version}.tar.gz
# build information
BuildPreReq: OpenPKG, openpkg >= 20160101
PreReq: OpenPKG, openpkg >= 20160101
%description
This library is a C port of the implementation of Limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method written by Jorge
Nocedal. The L-BFGS method solves the unconstrainted minimization
problem, minimize F(x), x = (x1, x2, ..., xN), only if the objective
function F(x) and its gradient G(x) are computable. The well-known
Newton's method requires computation of the inverse of the hessian
matrix of the objective function. However, the computational
cost for the inverse hessian matrix is expensive especially when
the objective function takes a large number of variables. The
L-BFGS method iteratively finds a minimizer by approximating the
inverse hessian matrix by information from last m iterations.
This innovation saves the memory storage and computational time
drastically for large-scaled problems.
%track
prog liblbfgs = {
version = %{version}
url = http://www.chokkan.org/software/liblbfgs/
regex = liblbfgs-(__VER__)\.tar\.gz
}
%prep
%setup -q
%build
CC="%{l_cc}" \
CFLAGS="%{l_cflags -O}" \
CPPFLAGS="%{l_cppflags}" \
LDFLAGS="%{l_ldflags}" \
./configure \
--prefix=%{l_prefix} \
--disable-shared \
--disable-nls
%{l_make} %{l_mflags -O}
%install
%{l_make} %{l_mflags} install AM_MAKEFLAGS="DESTDIR=$RPM_BUILD_ROOT"
rm -rf $RPM_BUILD_ROOT%{l_prefix}/share/doc
%{l_rpmtool} files -v -ofiles -r$RPM_BUILD_ROOT %{l_files_std}
%files -f files
%clean