2013-09-30 08:27:43 +08:00
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// loss of use, data, or profits; or business interruption) however caused
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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2013-06-20 19:54:09 +08:00
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#include "precomp.hpp"
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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#include <climits>
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#include <algorithm>
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2013-06-25 01:27:11 +08:00
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2014-08-14 16:50:07 +08:00
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#define dprintf(x)
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#define print_matrix(x)
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2014-08-14 17:18:04 +08:00
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namespace cv
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{
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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using std::vector;
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2013-06-25 01:27:11 +08:00
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2013-07-11 01:11:52 +08:00
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#ifdef ALEX_DEBUG
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2013-07-12 03:05:14 +08:00
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static void print_simplex_state(const Mat& c,const Mat& b,double v,const std::vector<int> N,const std::vector<int> B){
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printf("\tprint simplex state\n");
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2013-08-27 17:57:24 +08:00
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2013-07-19 08:09:39 +08:00
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printf("v=%g\n",v);
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2013-08-27 17:57:24 +08:00
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2013-07-12 03:05:14 +08:00
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printf("here c goes\n");
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2013-07-19 08:09:39 +08:00
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print_matrix(c);
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2013-08-27 17:57:24 +08:00
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2013-07-12 03:05:14 +08:00
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printf("non-basic: ");
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2013-07-19 17:34:33 +08:00
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print(Mat(N));
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2013-07-12 03:05:14 +08:00
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printf("\n");
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2013-08-27 17:57:24 +08:00
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2013-07-12 03:05:14 +08:00
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printf("here b goes\n");
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2013-07-19 08:09:39 +08:00
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print_matrix(b);
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2013-07-12 03:05:14 +08:00
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printf("basic: ");
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2013-08-27 17:57:24 +08:00
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2013-07-19 17:34:33 +08:00
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print(Mat(B));
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2013-07-12 03:05:14 +08:00
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printf("\n");
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}
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2013-07-11 17:29:55 +08:00
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#else
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2013-07-19 08:09:39 +08:00
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#define print_simplex_state(c,b,v,N,B)
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2013-07-11 01:11:52 +08:00
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#endif
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2013-07-03 18:54:23 +08:00
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2013-07-11 01:11:52 +08:00
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/**Due to technical considerations, the format of input b and c is somewhat special:
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*both b and c should be one column bigger than corresponding b and c of linear problem and the leftmost column will be used internally
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by this procedure - it should not be cleaned before the call to procedure and may contain mess after
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it also initializes N and B and does not make any assumptions about their init values
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* @return SOLVELP_UNFEASIBLE if problem is unfeasible, 0 if feasible.
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*/
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2013-07-20 20:14:02 +08:00
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static int initialize_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& B,vector<unsigned int>& indexToRow);
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static inline void pivot(Mat_<double>& c,Mat_<double>& b,double& v,vector<int>& N,vector<int>& B,int leaving_index,
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int entering_index,vector<unsigned int>& indexToRow);
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2013-07-11 01:11:52 +08:00
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/**@return SOLVELP_UNBOUNDED means the problem is unbdd, SOLVELP_MULTI means multiple solutions, SOLVELP_SINGLE means one solution.
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*/
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2013-07-20 20:14:02 +08:00
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static int inner_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& B,vector<unsigned int>& indexToRow);
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2013-07-11 14:52:13 +08:00
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static void swap_columns(Mat_<double>& A,int col1,int col2);
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2013-07-20 20:14:02 +08:00
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#define SWAP(type,a,b) {type tmp=(a);(a)=(b);(b)=tmp;}
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2013-07-03 18:54:23 +08:00
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//return codes:-2 (no_sol - unbdd),-1(no_sol - unfsbl), 0(single_sol), 1(multiple_sol=>least_l2_norm)
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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int solveLP(const Mat& Func, const Mat& Constr, Mat& z){
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2013-07-11 17:29:55 +08:00
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dprintf(("call to solveLP\n"));
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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2013-07-11 01:11:52 +08:00
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//sanity check (size, type, no. of channels)
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2013-07-12 03:05:14 +08:00
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CV_Assert(Func.type()==CV_64FC1 || Func.type()==CV_32FC1);
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CV_Assert(Constr.type()==CV_64FC1 || Constr.type()==CV_32FC1);
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CV_Assert((Func.rows==1 && (Constr.cols-Func.cols==1))||
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(Func.cols==1 && (Constr.cols-Func.rows==1)));
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2013-07-03 18:54:23 +08:00
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//copy arguments for we will shall modify them
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2013-07-12 03:05:14 +08:00
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Mat_<double> bigC=Mat_<double>(1,(Func.rows==1?Func.cols:Func.rows)+1),
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2013-07-03 18:54:23 +08:00
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bigB=Mat_<double>(Constr.rows,Constr.cols+1);
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2013-07-12 03:05:14 +08:00
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if(Func.rows==1){
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Func.convertTo(bigC.colRange(1,bigC.cols),CV_64FC1);
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}else{
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2013-07-19 17:34:33 +08:00
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Mat FuncT=Func.t();
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FuncT.convertTo(bigC.colRange(1,bigC.cols),CV_64FC1);
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2013-07-12 03:05:14 +08:00
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}
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Constr.convertTo(bigB.colRange(1,bigB.cols),CV_64FC1);
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2013-07-03 18:54:23 +08:00
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double v=0;
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vector<int> N,B;
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2013-07-20 20:14:02 +08:00
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vector<unsigned int> indexToRow;
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2013-07-03 18:54:23 +08:00
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2013-07-20 20:14:02 +08:00
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if(initialize_simplex(bigC,bigB,v,N,B,indexToRow)==SOLVELP_UNFEASIBLE){
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2013-07-11 01:11:52 +08:00
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return SOLVELP_UNFEASIBLE;
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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}
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2013-07-03 18:54:23 +08:00
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Mat_<double> c=bigC.colRange(1,bigC.cols),
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b=bigB.colRange(1,bigB.cols);
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int res=0;
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2013-07-20 20:14:02 +08:00
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if((res=inner_simplex(c,b,v,N,B,indexToRow))==SOLVELP_UNBOUNDED){
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2013-07-11 01:11:52 +08:00
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return SOLVELP_UNBOUNDED;
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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}
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2013-07-03 18:54:23 +08:00
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//return the optimal solution
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2013-07-12 03:05:14 +08:00
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z.create(c.cols,1,CV_64FC1);
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2013-07-03 18:54:23 +08:00
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MatIterator_<double> it=z.begin<double>();
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2013-10-04 21:03:15 +08:00
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unsigned int nsize = (unsigned int)N.size();
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2013-07-03 18:54:23 +08:00
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for(int i=1;i<=c.cols;i++,it++){
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2013-10-04 21:03:15 +08:00
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if(indexToRow[i]<nsize){
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2013-07-03 18:54:23 +08:00
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*it=0;
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}else{
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2013-10-04 21:03:15 +08:00
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*it=b.at<double>(indexToRow[i]-nsize,b.cols-1);
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2013-07-03 18:54:23 +08:00
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}
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}
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return res;
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}
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2013-07-20 20:14:02 +08:00
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static int initialize_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& B,vector<unsigned int>& indexToRow){
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2013-07-03 18:54:23 +08:00
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N.resize(c.cols);
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N[0]=0;
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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for (std::vector<int>::iterator it = N.begin()+1 ; it != N.end(); ++it){
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*it=it[-1]+1;
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}
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2013-07-03 18:54:23 +08:00
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B.resize(b.rows);
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2013-10-04 21:03:15 +08:00
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B[0]=(int)N.size();
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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for (std::vector<int>::iterator it = B.begin()+1 ; it != B.end(); ++it){
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*it=it[-1]+1;
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}
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2013-07-20 20:14:02 +08:00
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indexToRow.resize(c.cols+b.rows);
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indexToRow[0]=0;
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for (std::vector<unsigned int>::iterator it = indexToRow.begin()+1 ; it != indexToRow.end(); ++it){
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*it=it[-1]+1;
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}
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2013-07-03 18:54:23 +08:00
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v=0;
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The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
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2013-07-03 18:54:23 +08:00
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int k=0;
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{
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double min=DBL_MAX;
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for(int i=0;i<b.rows;i++){
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if(b(i,b.cols-1)<min){
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min=b(i,b.cols-1);
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k=i;
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}
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}
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}
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if(b(k,b.cols-1)>=0){
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N.erase(N.begin());
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2013-07-20 20:14:02 +08:00
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for (std::vector<unsigned int>::iterator it = indexToRow.begin()+1 ; it != indexToRow.end(); ++it){
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--(*it);
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}
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2013-07-03 18:54:23 +08:00
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return 0;
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}
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Mat_<double> old_c=c.clone();
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c=0;
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c(0,0)=-1;
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for(int i=0;i<b.rows;i++){
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b(i,0)=-1;
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}
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print_simplex_state(c,b,v,N,B);
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2013-07-11 17:29:55 +08:00
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dprintf(("\tWE MAKE PIVOT\n"));
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2013-07-20 20:14:02 +08:00
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pivot(c,b,v,N,B,k,0,indexToRow);
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2013-07-03 18:54:23 +08:00
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print_simplex_state(c,b,v,N,B);
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2013-07-20 20:14:02 +08:00
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inner_simplex(c,b,v,N,B,indexToRow);
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2013-07-03 18:54:23 +08:00
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2013-07-11 17:29:55 +08:00
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dprintf(("\tAFTER INNER_SIMPLEX\n"));
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2013-07-03 18:54:23 +08:00
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print_simplex_state(c,b,v,N,B);
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|
|
2013-10-04 21:03:15 +08:00
|
|
|
unsigned int nsize = (unsigned int)N.size();
|
|
|
|
if(indexToRow[0]>=nsize){
|
|
|
|
int iterator_offset=indexToRow[0]-nsize;
|
2013-07-11 17:29:55 +08:00
|
|
|
if(b(iterator_offset,b.cols-1)>0){
|
2013-07-11 01:11:52 +08:00
|
|
|
return SOLVELP_UNFEASIBLE;
|
2013-07-03 18:54:23 +08:00
|
|
|
}
|
2013-07-20 20:14:02 +08:00
|
|
|
pivot(c,b,v,N,B,iterator_offset,0,indexToRow);
|
2013-07-03 18:54:23 +08:00
|
|
|
}
|
|
|
|
|
2013-07-20 20:14:02 +08:00
|
|
|
vector<int>::iterator iterator;
|
2013-07-11 17:29:55 +08:00
|
|
|
{
|
2013-07-20 20:14:02 +08:00
|
|
|
int iterator_offset=indexToRow[0];
|
|
|
|
iterator=N.begin()+iterator_offset;
|
2013-07-11 17:29:55 +08:00
|
|
|
std::iter_swap(iterator,N.begin());
|
2013-07-20 20:14:02 +08:00
|
|
|
SWAP(int,indexToRow[*iterator],indexToRow[0]);
|
2013-07-11 17:29:55 +08:00
|
|
|
swap_columns(c,iterator_offset,0);
|
|
|
|
swap_columns(b,iterator_offset,0);
|
|
|
|
}
|
2013-07-03 18:54:23 +08:00
|
|
|
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("after swaps\n"));
|
2013-07-03 18:54:23 +08:00
|
|
|
print_simplex_state(c,b,v,N,B);
|
|
|
|
|
|
|
|
//start from 1, because we ignore x_0
|
|
|
|
c=0;
|
|
|
|
v=0;
|
2013-07-11 17:29:55 +08:00
|
|
|
for(int I=1;I<old_c.cols;I++){
|
2013-10-04 21:03:15 +08:00
|
|
|
if(indexToRow[I]<nsize){
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("I=%d from nonbasic\n",I));
|
2013-07-20 20:14:02 +08:00
|
|
|
int iterator_offset=indexToRow[I];
|
2013-08-27 17:57:24 +08:00
|
|
|
c(0,iterator_offset)+=old_c(0,I);
|
2013-07-03 18:54:23 +08:00
|
|
|
print_matrix(c);
|
|
|
|
}else{
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("I=%d from basic\n",I));
|
2013-10-04 21:03:15 +08:00
|
|
|
int iterator_offset=indexToRow[I]-nsize;
|
2013-07-11 17:29:55 +08:00
|
|
|
c-=old_c(0,I)*b.row(iterator_offset).colRange(0,b.cols-1);
|
|
|
|
v+=old_c(0,I)*b(iterator_offset,b.cols-1);
|
2013-07-03 18:54:23 +08:00
|
|
|
print_matrix(c);
|
|
|
|
}
|
|
|
|
}
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("after restore\n"));
|
2013-07-03 18:54:23 +08:00
|
|
|
print_simplex_state(c,b,v,N,B);
|
|
|
|
|
|
|
|
N.erase(N.begin());
|
2013-07-20 20:14:02 +08:00
|
|
|
for (std::vector<unsigned int>::iterator it = indexToRow.begin()+1 ; it != indexToRow.end(); ++it){
|
|
|
|
--(*it);
|
|
|
|
}
|
2013-07-03 18:54:23 +08:00
|
|
|
return 0;
|
|
|
|
}
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
|
2013-07-20 20:14:02 +08:00
|
|
|
static int inner_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& B,vector<unsigned int>& indexToRow){
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
int count=0;
|
2013-07-30 09:14:36 +08:00
|
|
|
for(;;){
|
2013-07-11 19:43:48 +08:00
|
|
|
dprintf(("iteration #%d\n",count));
|
|
|
|
count++;
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
|
2013-07-11 01:11:52 +08:00
|
|
|
static MatIterator_<double> pos_ptr;
|
2013-07-03 18:54:23 +08:00
|
|
|
int e=-1,pos_ctr=0,min_var=INT_MAX;
|
|
|
|
bool all_nonzero=true;
|
|
|
|
for(pos_ptr=c.begin();pos_ptr!=c.end();pos_ptr++,pos_ctr++){
|
|
|
|
if(*pos_ptr==0){
|
|
|
|
all_nonzero=false;
|
|
|
|
}
|
|
|
|
if(*pos_ptr>0){
|
|
|
|
if(N[pos_ctr]<min_var){
|
|
|
|
e=pos_ctr;
|
|
|
|
min_var=N[pos_ctr];
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if(e==-1){
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("hello from e==-1\n"));
|
2013-07-03 18:54:23 +08:00
|
|
|
print_matrix(c);
|
|
|
|
if(all_nonzero==true){
|
2013-07-11 01:11:52 +08:00
|
|
|
return SOLVELP_SINGLE;
|
2013-07-03 18:54:23 +08:00
|
|
|
}else{
|
2013-07-11 01:11:52 +08:00
|
|
|
return SOLVELP_MULTI;
|
2013-07-03 18:54:23 +08:00
|
|
|
}
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}
|
2013-07-03 18:54:23 +08:00
|
|
|
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
int l=-1;
|
2013-07-03 18:54:23 +08:00
|
|
|
min_var=INT_MAX;
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
double min=DBL_MAX;
|
|
|
|
int row_it=0;
|
2013-07-03 18:54:23 +08:00
|
|
|
MatIterator_<double> min_row_ptr=b.begin();
|
|
|
|
for(MatIterator_<double> it=b.begin();it!=b.end();it+=b.cols,row_it++){
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
double myite=0;
|
2013-07-03 18:54:23 +08:00
|
|
|
//check constraints, select the tightest one, reinforcing Bland's rule
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
if((myite=it[e])>0){
|
|
|
|
double val=it[b.cols-1]/myite;
|
2013-07-03 18:54:23 +08:00
|
|
|
if(val<min || (val==min && B[row_it]<min_var)){
|
|
|
|
min_var=B[row_it];
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
min_row_ptr=it;
|
|
|
|
min=val;
|
|
|
|
l=row_it;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if(l==-1){
|
2013-07-11 01:11:52 +08:00
|
|
|
return SOLVELP_UNBOUNDED;
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("the tightest constraint is in row %d with %g\n",l,min));
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
|
2013-07-20 20:14:02 +08:00
|
|
|
pivot(c,b,v,N,B,l,e,indexToRow);
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("objective, v=%g\n",v));
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
print_matrix(c);
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("constraints\n"));
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
print_matrix(b);
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("non-basic: "));
|
2013-07-19 17:34:33 +08:00
|
|
|
print_matrix(Mat(N));
|
|
|
|
dprintf(("basic: "));
|
|
|
|
print_matrix(Mat(B));
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}
|
2013-07-03 18:54:23 +08:00
|
|
|
}
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
|
2013-08-27 17:57:24 +08:00
|
|
|
static inline void pivot(Mat_<double>& c,Mat_<double>& b,double& v,vector<int>& N,vector<int>& B,
|
2013-07-20 20:14:02 +08:00
|
|
|
int leaving_index,int entering_index,vector<unsigned int>& indexToRow){
|
2013-07-11 17:29:55 +08:00
|
|
|
double Coef=b(leaving_index,entering_index);
|
2013-07-03 18:54:23 +08:00
|
|
|
for(int i=0;i<b.cols;i++){
|
|
|
|
if(i==entering_index){
|
2013-07-11 17:29:55 +08:00
|
|
|
b(leaving_index,i)=1/Coef;
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}else{
|
2013-07-11 17:29:55 +08:00
|
|
|
b(leaving_index,i)/=Coef;
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2013-07-03 18:54:23 +08:00
|
|
|
for(int i=0;i<b.rows;i++){
|
|
|
|
if(i!=leaving_index){
|
|
|
|
double coef=b(i,entering_index);
|
|
|
|
for(int j=0;j<b.cols;j++){
|
|
|
|
if(j==entering_index){
|
|
|
|
b(i,j)=-coef*b(leaving_index,j);
|
|
|
|
}else{
|
|
|
|
b(i,j)-=(coef*b(leaving_index,j));
|
|
|
|
}
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}
|
|
|
|
}
|
2013-07-03 18:54:23 +08:00
|
|
|
}
|
|
|
|
|
|
|
|
//objective function
|
2013-07-11 17:29:55 +08:00
|
|
|
Coef=c(0,entering_index);
|
2013-07-03 18:54:23 +08:00
|
|
|
for(int i=0;i<(b.cols-1);i++){
|
|
|
|
if(i==entering_index){
|
2013-07-11 17:29:55 +08:00
|
|
|
c(0,i)=-Coef*b(leaving_index,i);
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}else{
|
2013-07-11 17:29:55 +08:00
|
|
|
c(0,i)-=Coef*b(leaving_index,i);
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}
|
|
|
|
}
|
2013-07-11 17:29:55 +08:00
|
|
|
dprintf(("v was %g\n",v));
|
|
|
|
v+=Coef*b(leaving_index,b.cols-1);
|
2013-08-27 17:57:24 +08:00
|
|
|
|
2013-07-20 20:14:02 +08:00
|
|
|
SWAP(int,N[entering_index],B[leaving_index]);
|
|
|
|
SWAP(int,indexToRow[N[entering_index]],indexToRow[B[leaving_index]]);
|
The first draft of simplex algorithm, simple tests.
What we have now corresponds to "formal simplex algorithm", described in
Cormen's "Intro to Algorithms". It will work *only* if the initial
problem has (0,0,0,...,0) as feasible solution (consequently, it will
work unpredictably if problem was unfeasible or did not have zero-vector as
feasible solution). Moreover, it might cycle.
TODO (first priority)
1. Implement initialize_simplex() procedure, that shall check for
feasibility and generate initial feasible solution. (in particular, code
should pass all 4 tests implemented at the moment)
2. Implement Bland's rule to avoid cycling.
3. Make the code more clear.
4. Implement several non-trivial tests (??) and check algorithm against
them. Debug if necessary.
TODO (second priority)
1. Concentrate on stability and speed (make difficult tests)
2013-06-28 20:28:57 +08:00
|
|
|
}
|
2013-06-25 01:27:11 +08:00
|
|
|
|
2013-07-11 14:52:13 +08:00
|
|
|
static inline void swap_columns(Mat_<double>& A,int col1,int col2){
|
2013-07-03 18:54:23 +08:00
|
|
|
for(int i=0;i<A.rows;i++){
|
|
|
|
double tmp=A(i,col1);
|
|
|
|
A(i,col1)=A(i,col2);
|
|
|
|
A(i,col2)=tmp;
|
|
|
|
}
|
|
|
|
}
|
2014-08-14 16:50:07 +08:00
|
|
|
}
|