Awesome

pyOpt

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.

Building and installing

Requirements:

python
numpy and numpy-ext
fortran compiler
swig

Build commands

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

Licensing

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.)

Contact and Feedback

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