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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2013-07-11 01:11:52 +08:00
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#include <cstdarg>
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2013-06-25 01:27:11 +08:00
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2013-07-19 08:09:39 +08:00
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#define ALEX_DEBUG
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2013-06-25 01:27:11 +08:00
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namespace cv{namespace optim{
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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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2013-07-19 08:09:39 +08:00
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using namespace std;
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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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2013-07-11 17:29:55 +08:00
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#define dprintf(x) printf x
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2013-07-12 03:05:14 +08:00
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static void print_matrix(const Mat& x){
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2013-07-19 17:34:33 +08:00
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print(x);
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printf("\n");
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2013-07-12 03:05:14 +08:00
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}
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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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2013-07-19 08:09:39 +08:00
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printf("v=%g\n",v);
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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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2013-07-12 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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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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2013-07-19 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 dprintf(x)
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#define print_matrix(x)
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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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2013-07-11 14:52:13 +08:00
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static int initialize_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& B);
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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,int entering_index);
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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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2013-07-11 14:52:13 +08:00
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static int inner_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& B);
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static void swap_columns(Mat_<double>& A,int col1,int col2);
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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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2013-07-11 01:11:52 +08:00
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if(initialize_simplex(bigC,bigB,v,N,B)==SOLVELP_UNFEASIBLE){
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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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2013-07-11 01:11:52 +08:00
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if((res=inner_simplex(c,b,v,N,B))==SOLVELP_UNBOUNDED){
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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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for(int i=1;i<=c.cols;i++,it++){
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std::vector<int>::iterator pos=B.begin();
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if((pos=std::find(B.begin(),B.end(),i))==B.end()){
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*it=0;
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}else{
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*it=b.at<double>(pos-B.begin(),b.cols-1);
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}
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}
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return res;
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}
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2013-07-11 14:52:13 +08:00
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static int initialize_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& B){
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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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B[0]=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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2013-07-03 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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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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2013-07-03 18:54:23 +08:00
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pivot(c,b,v,N,B,k,0);
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print_simplex_state(c,b,v,N,B);
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inner_simplex(c,b,v,N,B);
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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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2013-07-11 17:29:55 +08:00
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vector<int>::iterator iterator=std::find(B.begin(),B.end(),0);
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if(iterator!=B.end()){
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int iterator_offset=iterator-B.begin();
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if(b(iterator_offset,b.cols-1)>0){
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2013-07-11 01:11:52 +08:00
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return SOLVELP_UNFEASIBLE;
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2013-07-03 18:54:23 +08:00
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}
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2013-07-11 17:29:55 +08:00
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pivot(c,b,v,N,B,iterator_offset,0);
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2013-07-03 18:54:23 +08:00
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}
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2013-07-11 17:29:55 +08:00
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{
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iterator=std::find(N.begin(),N.end(),0);
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int iterator_offset=iterator-N.begin();
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std::iter_swap(iterator,N.begin());
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swap_columns(c,iterator_offset,0);
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swap_columns(b,iterator_offset,0);
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}
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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(("after swaps\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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//start from 1, because we ignore x_0
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c=0;
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v=0;
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2013-07-11 17:29:55 +08:00
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for(int I=1;I<old_c.cols;I++){
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if((iterator=std::find(N.begin(),N.end(),I))!=N.end()){
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dprintf(("I=%d from nonbasic\n",I));
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int iterator_offset=iterator-N.begin();
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c(0,iterator_offset)+=old_c(0,I);
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2013-07-03 18:54:23 +08:00
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print_matrix(c);
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}else{
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2013-07-11 17:29:55 +08:00
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dprintf(("I=%d from basic\n",I));
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int iterator_offset=std::find(B.begin(),B.end(),I)-B.begin();
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c-=old_c(0,I)*b.row(iterator_offset).colRange(0,b.cols-1);
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v+=old_c(0,I)*b(iterator_offset,b.cols-1);
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2013-07-03 18:54:23 +08:00
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print_matrix(c);
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}
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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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2013-07-11 17:29:55 +08:00
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dprintf(("after restore\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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N.erase(N.begin());
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|
|
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
|
|
|
|
2013-07-11 14:52:13 +08:00
|
|
|
static int inner_simplex(Mat_<double>& c, Mat_<double>& b,double& v,vector<int>& N,vector<int>& 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
|
|
|
int count=0;
|
|
|
|
while(1){
|
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
|
|
|
|
2013-07-11 01:11:52 +08:00
|
|
|
pivot(c,b,v,N,B,l,e);
|
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
|
|
|
|
2013-07-11 14:52:13 +08:00
|
|
|
static inline void pivot(Mat_<double>& c,Mat_<double>& b,double& v,vector<int>& N,vector<int>& B, int leaving_index,int entering_index){
|
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);
|
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 tmp=N[entering_index];
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N[entering_index]=B[leaving_index];
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B[leaving_index]=tmp;
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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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2013-06-25 01:27:11 +08:00
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2013-07-11 14:52:13 +08:00
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static inline void swap_columns(Mat_<double>& A,int col1,int col2){
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2013-07-03 18:54:23 +08:00
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for(int i=0;i<A.rows;i++){
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double tmp=A(i,col1);
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A(i,col1)=A(i,col2);
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A(i,col2)=tmp;
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}
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}
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2013-06-25 01:27:11 +08:00
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}}
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