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 the QUICKGUIDE file for further details.
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 (Ruben.Perez@rmc.ca)
- Peter W. Jansen (Peter.Jansen@rmc.ca)