Skip to content

Readme.macosx.md

Latest commit

 

History

History
130 lines (95 loc) · 3.56 KB

Readme.macosx.md

File metadata and controls

130 lines (95 loc) · 3.56 KB

Build instructions for Mac OS/X users

If you require one of ASL, pyadolc, pycppad, MUMPS, qr_mumps or SuiteSparse, installing NLP.py will be much easier if you use Homebrew. Follow the instructions to install Homebrew and import the science formulas:

brew tap homebrew/science

Requirements

Install numpy and PyKrylov:

pip install -q numpy
pip install -q git+https://github.com/PythonOptimizers/pykrylov.git@develop

Optional Dependencies

Sparse Matrix Storage

CySparse

pip install -q git+https://github.com/PythonOptimizers/cysparse.git

PySparse

pip install -q git+https://github.com/optimizers/pysparse.git

SciPy

pip install -q scipy

Derivatives Computation

ASL

The ASL will allow AMPL models to be loaded in NLP.py after they have been decoded to a .nl file. Creating the .nl file for anything else than small models requires an AMPL license. A few sample .nl files are included with NLP.py. The ASL will compute sparse first and second derivatives.

The ASL may be installed from Homebrew:

brew install asl

Specify the location of the ASL in setup.cfg:

echo "[ASL]" > site.cfg
echo "asl_dir = $(brew --prefix asl)" >> site.cfg

ADOL-C

ADOL-C will allow models to be coded up directly in Python and sparse first and second derivatives to be computed via automatic differentiation. This is the preferred way to model large-scale problems in Python. First install ADOL-C:

brew install adol-c  # will also install Colpack
brew install boost-python

Then install pyadolc:

cd $HOME  # or another local
git clone https://github.com/b45ch1/pyadolc.git && cd pyadolc
python setup.py install  # press [Enter] when prompted

CppAD

CppAD will allow models to be coded up directly in Python and dense first and second derivatives to be computed via automatic differentiation.

First install CppAD:

brew install cppad [--with-adol-c] --with-openmp

Then install pycppad:

pip install -q git+https://github.com/b45ch1/pycppad.git

AlgoPy

AlgoPy will allow models to be coded up directly in Python and dense first and second derivatives to be computed via automatic differentiation.

pip install -q algopy

Factorizations

HSL.py

Follow instructions at https://github.com/PythonOptimizers/HSL.py

MUMPS.py

Follow instructions at https://github.com/PythonOptimizers/MUMPS.py

qr_mumps.py

Follow instructions at https://github.com/PythonOptimizers/qr_mumps.py

SuiteSparse.py

Follow instructions at https://github.com/PythonOptimizers/SuiteSparse.py

Troubleshooting

If you encounter build errors while installing pyadolc, edit and change setup.py as follows:

diff --git a/setup.py b/setup.py
index 5e6e695..68f5c68 100644
--- a/setup.py
+++ b/setup.py
@@ -36,7 +36,7 @@ colpack_lib_path1    = os.path.join(COLPACK_DIR, 'lib')
 colpack_lib_path2    = os.path.join(COLPACK_DIR, 'lib64')

 # ADAPT THIS TO FIT YOUR SYSTEM
-extra_compile_args = ['-std=c++11 -ftemplate-depth-100 -DBOOST_PYTHON_DYNAMIC_LIB']
+extra_compile_args = ['-std=c++11 -stdlib=libc++ -mmacosx-version-min=10.9 -ftemplate-depth-100 -DBOOST_PYTHON_DYNAMIC_LIB']
 include_dirs = [get_numpy_include_dirs()[0], boost_include_path, adolc_include_path, colpack_include_path]
 library_dirs = [boost_library_path1, boost_library_path2, adolc_library_path1, adolc_library_path2, colpack_lib_path1, colpack_lib_path2]
 libraries = ['boost_python','adolc', 'ColPack']