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)

REQUIREMENTS:

• Known to work for Ubuntu Linux, Python 2.6, NumPy 1.3.0, Boost:Python 1.40.0
• Python and Numpy, both with header files
• ADOL-C version 2.1.0 http://www.coin-or.org/projects/ADOL-C.xml
• boost::python from http://www.boost.org/

OPTIONAL REQUIREMENTS:

• For sparse Jacobians and Hessians: ColPack 1.0.0 http://www.cscapes.org/coloringpage/software.htm
• scons build tool (makes things easier if you need to recompile pyadolc)

INSTALLATION:

• CHECK REQUIREMENTS: Make sure you have ADOL-C (version 2.1 and above), ColPack (version 1.0.0 and above) the boost libraries and numpy installed. All with header files.

• BUILD COLPACK

  • run make
  • this should generate ~workspace/ColPack/build/lib/libColPack.so.

• BUILD ADOL-C:

  • run ./configure --enable-sparse --with-colpack=/home/b45ch1/workspace/ColPack/build
  • REMARK: the option --enable-sparse is used in ADOLC-2.2.1. In ADOLC-2.1.0 it is called --with-sparse.
  • run make
  • You don't have to run make install.
  • You should then have a folder ~/workspace/ADOL-C-2.1.0/ADOL-C with adolc/adolc.h in it.

• DOWNLAD PYADOLC: cd ~ and then git clone git://github.com/b45ch1/pyadolc.git

• BUILD PYADOLC:

  • change to the folder ~/pyadolc and rename the file setup.py.EXAMPLE to setup.py.
  • Adapt setup.py to fit your system. In particular, you have to set the paths to your ADOL-C installation and boost python.
  • run python setup.py build. A new folder with a name similar to ~/pyadolc/build/lib.linux-x86_64-2.6 should be generated.
  • run python setup.py install to install pyadolc to your system.

• TEST YOUR INSTALLATION:

  • Change directory to ~/pyadolc/build/lib.linux-x86_64-2.6
  • run python -c "import adolc; adolc.test()". All tests should pass.

• You can also use scons (if you have it) instead of using setup.py
• If anything goes wrong, please file a bug report.