PYADOLC, a wrapper for ADOL-C

Short Description:

This PYADOLC, a Python module to differentiate complex algorithms written in Python. It wraps the functionality of the library ADOL-C (C++).

Author:

Sebastian F. Walter

Licence (new BSD):

Copyright (c) 2008, Sebastian F. Walter All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the HU Berlin nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY Sebastian F. Walter ''AS IS'' AND ANY EXPRESS 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 Sebastian F. Walter 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.

EXAMPLE USAGE:

import numpy
from adolc import *
N = M = 10
A = numpy.zeros((M,N))
A[:] = [[ 1./N +(n==m) for n in range(N)] for m in range(M)]
def f(x):
    return numpy.dot(A,x)

# tape a function evaluation
ax = numpy.array([adouble(0) for n in range(N)])
trace_on(1)
independent(ax)
ay = f(ax)
dependent(ay)
trace_off()

x = numpy.array([n+1 for n in range(N)])

# compute jacobian of f at x
J = jacobian(1,x)

# compute gradient of f at x
if M==1:
    g = gradient(1,x)

THIS VERSION OF PYADOLC IS KNOWN TO WORK WITH:

• Ubuntu Linux, Python 2.7.3, NumPy 1.8.0
• Debian Stretch Linux, Python 2.7.13, NumPy 1.13.1
• OSX 10.9 (Mavericks), Python 2.7.6, NumPy 1.8.0
• OSX 10.11 (El Capitan), Python 2.7.11, NumPy 1.10.11

REQUIREMENTS:

• C and C++ compiler
• Python and Numpy, both with header files
• ADOL-C, official versions from http://www.coin-or.org/projects/ADOL-C.xml
• ColPack from http://cscapes.cs.purdue.edu/download/ColPack
• boost::python from http://www.boost.org/ or from the apt-get repository.

INSTALLATION UBUNTU / DEBIAN (Stretch):

• install boost-python via apt-get
• install autotools-dev libtool libboost-all-dev
• Use ./bootstrap.sh to download ADOL-C and ColPack and compile them.
• Run python setup.py and follow the instructions

INSTALLATION OSX:

• Run:

  brew install wget
  brew install automake
  brew install shtool
  brew install libtool
  brew install boost --with-python
  brew install boost-python
  brew install homebrew/science/adol-c
  

• Run CC=clang CXX=clang++ python setup.py

You may have to run``brew link automake`` to generate symbolic links.

TEST YOUR INSTALLATION:

• install nose, matplotlib, e.g., via pip install nose matplotlib
• add pyadolc to your python path
• run python -c "import adolc; adolc.test()". All tests should pass.
• If anything goes wrong, please file a bug report.

Warning

If you run the test from the root folder of pyadolc you will get ImportError: No module named _adolc since it first looks in the local folder ./adolc before trying the other directories in your PYTHONPATH.

MANUAL INSTALLATION:

Follow the steps in ./bootstrap.sh and adapt if necessary.