目标:在windows 7 (64bit) ,python2.7 环境下面安装cvxopt,以便进行线性规划、二次规划、半正定规划等的计算。
解决过程简略记录:参考了cvxopt的官方说明 来安装cvxopt,但卡在了LAPACK的编译结果liblapack.a不能正确输出上面,LAPACK啊LAPACK。。。
于是又参考了 《如何在Windows下使用LAPACK和ARPACK》 的工作,正确输出了liblapack.a,之后再按照cvxopt的官方说明进行就没有问题了。
整体的过程如下:
1,windows7上安装矩阵计算BLAS/LAPACK
1.1编译BLAS
下载BLAS的最新版源文件 并解压为BLAS文件夹。
cd BLAS
sed 's/_LINUX/_WIN/' make.inc -i
make && cp blas_WIN.a ../libblas.a
这样就得到了 libblas.a,在cvxopt的编译过程中需要。
1.2编译LAPACK
下载lapack的最新源文件,解压。
tar -xvf lapack-3.4.2.tgz
cd lapack-3.4.2
2,编译cvxopt
按照cvxopt的官方说明 进行。
(1)下载最新版cvxopt1.1.6源文件 。
(2)解压
tar -xvf cvxopt-1.1.6.tar.gz
cd cvxopt-1.1.6/src
(3)修改setup.py
- 修改 变量 BLAS_LIB = [‘blas’,’gfortran’]
- 修改变量 BLAS_LIB_DIR = ‘.’
sed 's/-mno-cygwin//g' -i'.bak' c:\Python27\Lib\distutils\cygwinccompiler.py
python setup.py build --compiler=mingw32
python setup.py install
(7)不要忘了把python的编译方式改回来
mv c:\Python27\Lib\distutils\cygwinccompiler.py.bak c:\Python27\Lib\distutils\cygwinccompiler.py
==================================================
下面运行cvxopt官方的一个线性规划的例子:
''' Created on 2013-9-11 @author: Waleking ''' from cvxopt import matrix, solvers if __name__ == '__main__': Q = 2*matrix([ [2, .5], [.5, 1] ]) p = matrix([1.0, 1.0]) G = matrix([[-1.0,0.0],[0.0,-1.0]]) h = matrix([0.0,0.0]) A = matrix([1.0, 1.0], (1,2)) b = matrix(1.0) sol=solvers.qp(Q, p, G, h, A, b) print(sol['x'])
pcost dcost gap pres dres
0: 1.8889e+00 7.7778e-01 1e+00 3e-16 2e+00
1: 1.8769e+00 1.8320e+00 4e-02 1e-16 6e-02
2: 1.8750e+00 1.8739e+00 1e-03 2e-16 5e-04
3: 1.8750e+00 1.8750e+00 1e-05 1e-16 5e-06
4: 1.8750e+00 1.8750e+00 1e-07 3e-16 5e-08
Optimal solution found.
[ 2.50e-01]
[ 7.50e-01]
附上cvxopt的setup.py:
from distutils.core import setup, Extension from glob import glob # Modifiy this if BLAS and LAPACK libraries are not in /usr/lib. #BLAS_LIB_DIR = '/usr/lib' BLAS_LIB_DIR = '.' # Default names of BLAS and LAPACK libraries BLAS_LIB = ['blas','gfortran'] #LAPACK_LIB = ['lapack','gfortran'] LAPACK_LIB = ['lapack'] BLAS_EXTRA_LINK_ARGS = [] # Set environment variable BLAS_NOUNDERSCORES=1 if your BLAS/LAPACK do # not use trailing underscores BLAS_NOUNDERSCORES = False # Set to 1 if you are using the random number generators in the GNU # Scientific Library. BUILD_GSL = 0 # Directory containing libgsl (used only when BUILD_GSL = 1). GSL_LIB_DIR = '/usr/lib' # Directory containing the GSL header files (used only when BUILD_GSL = 1). GSL_INC_DIR = '/usr/include/gsl' # Set to 1 if you are installing the fftw module. BUILD_FFTW = 0 # Directory containing libfftw3 (used only when BUILD_FFTW = 1). FFTW_LIB_DIR = '/usr/lib' # Directory containing fftw.h (used only when BUILD_FFTW = 1). FFTW_INC_DIR = '/usr/include' # Set to 1 if you are installing the glpk module. BUILD_GLPK = 0 # Directory containing libglpk (used only when BUILD_GLPK = 1). GLPK_LIB_DIR = '/usr/lib' # Directory containing glpk.h (used only when BUILD_GLPK = 1). GLPK_INC_DIR = '/usr/include' # Set to 1 if you are installing the DSDP module. BUILD_DSDP = 0 # Directory containing libdsdp (used only when BUILD_DSDP = 1). DSDP_LIB_DIR = '/usr/lib' # Directory containing dsdp5.h (used only when BUILD_DSDP = 1). DSDP_INC_DIR = '/usr/include/dsdp' # No modifications should be needed below this line. extmods = [] # Macros MACROS = [] if BLAS_NOUNDERSCORES: MACROS.append(('BLAS_NO_UNDERSCORE','')) # optional modules if BUILD_GSL: gsl = Extension('gsl', libraries = ['m', 'gsl'] + BLAS_LIB, include_dirs = [ GSL_INC_DIR ], library_dirs = [ GSL_LIB_DIR, BLAS_LIB_DIR ], extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = ['src/C/gsl.c'] ) extmods += [gsl]; if BUILD_FFTW: fftw = Extension('fftw', libraries = ['fftw3'] + BLAS_LIB, include_dirs = [ FFTW_INC_DIR ], library_dirs = [ FFTW_LIB_DIR, BLAS_LIB_DIR ], extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = ['src/C/fftw.c'] ) extmods += [fftw]; if BUILD_GLPK: glpk = Extension('glpk', libraries = ['glpk'], include_dirs = [ GLPK_INC_DIR ], library_dirs = [ GLPK_LIB_DIR ], sources = ['src/C/glpk.c'] ) extmods += [glpk]; if BUILD_DSDP: dsdp = Extension('dsdp', libraries = ['dsdp'] + LAPACK_LIB + BLAS_LIB, include_dirs = [ DSDP_INC_DIR ], library_dirs = [ DSDP_LIB_DIR, BLAS_LIB_DIR ], extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = ['src/C/dsdp.c'] ) extmods += [dsdp]; # Required modules base = Extension('base', libraries = ['m'] + LAPACK_LIB + BLAS_LIB, library_dirs = [ BLAS_LIB_DIR ], define_macros = MACROS, extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = ['src/C/base.c','src/C/dense.c','src/C/sparse.c']) blas = Extension('blas', libraries = BLAS_LIB, library_dirs = [ BLAS_LIB_DIR ], define_macros = MACROS, extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = ['src/C/blas.c'] ) lapack = Extension('lapack', libraries = LAPACK_LIB + BLAS_LIB, library_dirs = [ BLAS_LIB_DIR ], define_macros = MACROS, extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = ['src/C/lapack.c'] ) umfpack = Extension('umfpack', include_dirs = [ 'src/C/SuiteSparse/UMFPACK/Include', 'src/C/SuiteSparse/AMD/Include', 'src/C/SuiteSparse/AMD/Source', 'src/C/SuiteSparse/SuiteSparse_config' ], library_dirs = [ BLAS_LIB_DIR ], define_macros = MACROS + [('NTIMER', '1'), ('NCHOLMOD', '1')], libraries = LAPACK_LIB + BLAS_LIB, extra_compile_args = ['-Wno-unknown-pragmas'], extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = [ 'src/C/umfpack.c', 'src/C/SuiteSparse/UMFPACK/Source/umfpack_global.c', 'src/C/SuiteSparse/UMFPACK/Source/umfpack_tictoc.c' ] + ['src/C/SuiteSparse/SuiteSparse_config/SuiteSparse_config.c'] + glob('src/C/SuiteSparse_cvxopt_extra/umfpack/*')) # Build for int or long? import sys if sys.maxsize > 2**31: MACROS += [('DLONG',None)] cholmod = Extension('cholmod', library_dirs = [ BLAS_LIB_DIR ], libraries = LAPACK_LIB + BLAS_LIB, include_dirs = [ 'src/C/SuiteSparse/CHOLMOD/Include', 'src/C/SuiteSparse/COLAMD', 'src/C/SuiteSparse/AMD/Include', 'src/C/SuiteSparse/COLAMD/Include', 'src/C/SuiteSparse/SuiteSparse_config' ], define_macros = MACROS + [('NPARTITION', '1'), ('NTIMER', '1')], extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = [ 'src/C/cholmod.c' ] + ['src/C/SuiteSparse/AMD/Source/' + s for s in ['amd_global.c', 'amd_postorder.c', 'amd_post_tree.c', 'amd_2.c']] + ['src/C/SuiteSparse/COLAMD/Source/' + s for s in ['colamd.c', 'colamd_global.c']] + ['src/C/SuiteSparse/SuiteSparse_config/SuiteSparse_config.c'] + glob('src/C/SuiteSparse/CHOLMOD/Core/c*.c') + glob('src/C/SuiteSparse/CHOLMOD/Cholesky/c*.c') + ['src/C/SuiteSparse/CHOLMOD/Check/cholmod_check.c'] + glob('src/C/SuiteSparse/CHOLMOD/Supernodal/c*.c') ) amd = Extension('amd', include_dirs = [ 'src/C/SuiteSparse/AMD/Include', 'src/C/SuiteSparse/SuiteSparse_config' ], define_macros = MACROS, sources = [ 'src/C/amd.c' ] + glob('src/C/SuiteSparse/AMD/Source/*.c') ) misc_solvers = Extension('misc_solvers', libraries = LAPACK_LIB + BLAS_LIB, library_dirs = [ BLAS_LIB_DIR ], define_macros = MACROS, extra_link_args = BLAS_EXTRA_LINK_ARGS, sources = ['src/C/misc_solvers.c'] ) extmods += [base, blas, lapack, umfpack, cholmod, amd, misc_solvers] setup (name = 'cvxopt', description = 'Convex optimization package', version = '1.1.6', long_description = ''' CVXOPT is a free software package for convex optimization based on the Python programming language. It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. Its main purpose is to make the development of software for convex optimization applications straightforward by building on Python's extensive standard library and on the strengths of Python as a high-level programming language.''', author = 'M. Andersen, J. Dahl, and L. Vandenberghe', author_email = '[email protected], [email protected], [email protected]', url = 'http://abel.ee.ucla.edu/cvxopt', license = 'GNU GPL version 3', ext_package = "cvxopt", ext_modules = extmods, package_dir = {"cvxopt": "src/python"}, packages = ["cvxopt"])