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README for Gauss Pseudospectral Optimization Software (GPOPS):  
An Open-Source MATLAB Implementation of the Gauss Pseudospectral 
Method for Solving Multiple-Phase Optimal Control Problems 
Authors:  Anil V. Rao, David A. Benson, Geoffrey Huntington, 
          Christopher Darby, Michael Patterson, & Camila Francolin
Version Number: 2.1                            Date:  February 2009
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Copyright (c) 2008 by The Authors
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WARRANTY:  This software comes with absolutely no warranty that it
will work for any particular purpose.  Furthermore, the author is not
responsible for any harm that may result as a result of using this
software in any application and assumes no responsibility whatsoever
for any damage caused by the use of the software or any derivative
software.  
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GPOPS is an OPEN-SOURCE implementation of the Gauss pseudospectral 
method for solving multiple-phase optimal control problems.  GPOPS 
was written by the authors beginning in 2008.  The GPOPS code has
been written by the authors and all algorithms used in GPOPS are 
taken from the open literature.  No proprietary, confidential, or 
otherwise restricted information has been knowingly used by any of
the authors in the creation or development of GPOPS.
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REQUIRED THIRD-PARTY PRODUCTS:  GPOPS requires the NLP solver SNOPT.  
SNOPT can be obtained from either Stanford Business Software (for 
commercial, government, or commercial use) or by contacting Professor 
Philip Gill (for academic research purposes).  

RECOMMENDED THIRD-PARTY PRODUCTS:  While not required, to improve 
performance it is highly recommended that users obtain the automatic 
differentiation package INTLAB.  INTLAB is available for internal or 
non-commercial use by visiting the website 
http://www.ti3.tu-harburg.de/rump/intlab/.  Commercial or other use 
of INTLAB requires a license for which you must contact Professor 
Siegfried Rump at rump@tu-harburg.de.  

OPTIONAL THIRD-PARTY PRODUCTS:  An alternative to INTLAB is the 
commercially available automatic differentiation package Matlab 
Automatic Differentiation (MAD).  MAD is available for a nominal fee 
from TOMLAB Optimization and can be found at http://www.tomopt.com.
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