scalar2_nnc.cpp
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00001 /*
00002  *    This file is part of ACADO Toolkit.
00003  *
00004  *    ACADO Toolkit -- A Toolkit for Automatic Control and Dynamic Optimization.
00005  *    Copyright (C) 2008-2014 by Boris Houska, Hans Joachim Ferreau,
00006  *    Milan Vukov, Rien Quirynen, KU Leuven.
00007  *    Developed within the Optimization in Engineering Center (OPTEC)
00008  *    under supervision of Moritz Diehl. All rights reserved.
00009  *
00010  *    ACADO Toolkit is free software; you can redistribute it and/or
00011  *    modify it under the terms of the GNU Lesser General Public
00012  *    License as published by the Free Software Foundation; either
00013  *    version 3 of the License, or (at your option) any later version.
00014  *
00015  *    ACADO Toolkit is distributed in the hope that it will be useful,
00016  *    but WITHOUT ANY WARRANTY; without even the implied warranty of
00017  *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
00018  *    Lesser General Public License for more details.
00019  *
00020  *    You should have received a copy of the GNU Lesser General Public
00021  *    License along with ACADO Toolkit; if not, write to the Free Software
00022  *    Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
00023  *
00024  */
00025 
00026 
00034 #include <acado_optimal_control.hpp>
00035 #include <acado_gnuplot.hpp>
00036 
00037 
00038 /* >>> start tutorial code >>> */
00039 int main( ){
00040 
00041     USING_NAMESPACE_ACADO
00042 
00043 
00044     // INTRODUCE THE VARIABLES:
00045     // -------------------------
00046     Parameter y1,y2;
00047 
00048 
00049     // DEFINE AN OPTIMIZATION PROBLEM:
00050     // -------------------------------
00051     NLP nlp;
00052     nlp.minimize( 0, y1 );
00053     nlp.minimize( 1, y2 );
00054 
00055     nlp.subjectTo( 0.0 <= y1 <= 5.0 );
00056     nlp.subjectTo( 0.0 <= y2 <= 5.2 );
00057     nlp.subjectTo( 0.0 <= y2 - 5.0*exp(-y1) - 2.0*exp(-0.5*(y1-3.0)*(y1-3.0)) );
00058 
00059 
00060     // DEFINE A MULTI-OBJECTIVE ALGORITHM AND SOLVE THE NLP:
00061     // -----------------------------------------------------
00062     MultiObjectiveAlgorithm algorithm(nlp);
00063 
00064     algorithm.set( PARETO_FRONT_GENERATION, PFG_NORMALIZED_NORMAL_CONSTRAINT );
00065     algorithm.set( PARETO_FRONT_DISCRETIZATION, 41 );
00066     algorithm.set( KKT_TOLERANCE, 1e-12 );
00067 
00068     // Minimize individual objective function  
00069     algorithm.initializeParameters("initial_scalar2_2.txt");
00070     algorithm.solveSingleObjective(1);
00071 
00072     // Minimize individual objective function
00073     algorithm.solveSingleObjective(0);
00074 
00075     // Generate Pareto set 
00076     algorithm.initializeParameters("initial_scalar2_2.txt");
00077     //algorithm.set( PARETO_FRONT_HOTSTART      , BT_FALSE                         );
00078     algorithm.solve();
00079 
00080 
00081     // GET THE RESULT FOR THE PARETO FRONT AND PLOT IT:
00082     // ------------------------------------------------
00083     VariablesGrid paretoFront;
00084     algorithm.getParetoFront( paretoFront );
00085     algorithm.getWeights("scalar2_nnc_weights.txt");
00086 
00087     GnuplotWindow window1;
00088     window1.addSubplot( paretoFront, "Pareto Front y1 vs y2", "y1","y2", PM_POINTS );
00089     window1.plot( );
00090 
00091     paretoFront.print();
00092 
00093 
00094     // FILTER THE PARETO FRONT AND PLOT IT:
00095     // ------------------------------------
00096     algorithm.getParetoFrontWithFilter( paretoFront );
00097     algorithm.getWeightsWithFilter("scalar2_nnc_weights_filtered.txt");
00098 
00099     GnuplotWindow window2;
00100     window2.addSubplot( paretoFront, "Pareto Front (with filter) y1 vs y2", "y1","y2", PM_POINTS );
00101     window2.plot( );
00102 
00103     paretoFront.print();
00104 
00105 
00106     // PRINT INFORMATION ABOUT THE ALGORITHM:
00107     // --------------------------------------
00108     algorithm.printInfo();
00109 
00110     return 0;
00111 }
00112 /* <<< end tutorial code <<< */
00113 


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Thu Aug 27 2015 11:59:55