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