pyOpt - 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 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 (Ruben.Perez@rmc.ca)
    * Peter W. Jansen (Peter.Jansen@rmc.ca)