PYthon OPTimization Framework Copyright (c) 2008-2014, pyOpt Developers

pyOpt is an object-oriented framework for formulating and solving nonlinear constrained optimization problems.

Some of the features of pyOpt:

• Object-oriented development maintains independence between the optimization problem formulation and its solution by different optimizers
• Allows for easy integration of gradient-based, gradient-free, and population-based optimization algorithms
• Interfaces both open source as well as industrial optimizers
• Ease the work required to do nested optimization and provides automated solution refinement
• On parallel systems it enables the use of optimizers when running in a mpi parallel environment, allows for evaluation of gradients in parallel, and can distribute function evaluations for gradient-free optimizers
• Optimization solution histories can be stored during the optimization process. A partial history can also be used to warm-restart the optimization

see QUICKGUIDE.md for further details.

• python
• numpy and numpy-ext
• fortran compiler
• swig

Build default pyOpt

python setup.py build_ext --inplace

Build debug pyOpt with no optimization

python setup.py config_fc --debug --noopt build_ext --inplace

Get information about the available compilers

python setup.py config_fc --help-fcompiler

Distributed using the GNU Lesser General Public License (LGPL); see the LICENSE file for details.

Please cite pyOpt and the authors of the respective optimization algorithms in any publication for which you find it useful. (This is not a legal requirement, just a polite request.)

If you have questions, comments, problems, want to contribute to the framework development, or want to report a bug, please contact the main developers:

• Dr. Ruben E. Perez
• Peter W. Jansen