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View Code? Open in Web Editor NEWThis is a fork of pyOpt. It includes bug fixes so that pyOpt can do unconstrained optimization when using the COBYLA, CONMIN and SNOPT
License: Other
This is a fork of pyOpt. It includes bug fixes so that pyOpt can do unconstrained optimization when using the COBYLA, CONMIN and SNOPT
License: Other
pyOpt - PYthon OPTimization Framework ===================================== Copyright (c) 2008-2013, 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. 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: * Dr. Ruben E. Perez ([email protected]) * Peter W. Jansen ([email protected])
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