mirror of https://github.com/CGAL/cgal
85 lines
2.9 KiB
C++
85 lines
2.9 KiB
C++
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template < class Point >
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class PD_D_iterator {
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// types needed for iterators
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// ...
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typedef typename std::vector<Point>::const_iterator Point_it;
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typedef PD_D_row_iterator<Point> Row_it;
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// data members
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Point_it points; // iterator referring to C matrix
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int i; // row index
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public:
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PD_D_iterator( Point_it it) : points( it), i( 0) { }
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Row_it operator * ( ) { return Row_it( points, i);}
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// other operators required for random access iterators
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// ...
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};
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typedef CGAL::QP_transform_iterator_1< Matrix::const_iterator> Vector_iterator;
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struct QPSolverTraits {
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typedef GMP::Double ET;
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typedef Vector_iterator A_iterator;
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typedef CGAL::QP_const_value_iterator<IT> B_iterator;
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typedef CGAL::QP_const_value_iterator<IT> C_iterator;
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typedef PD_D_iterator<Point> D_iterator;
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enum Row_type { LESS_EQUAL, EQUAL, GREATER_EQUAL};
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typedef CGAL::QP_const_value_iterator<Row_type> Row_type_iterator;
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typedef CGAL::Tag_false Is_linear;
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typedef CGAL::Tag_true Is_symmetric;
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typedef CGAL::Tag_true Has_full_row_rank;
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typedef CGAL::Tag_false Use_perturbation;
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};
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typedef CGAL::QP_solver<QPSolverTraits> Solver;
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int main( int argc, char** argv)
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{
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Matrix A( 6);
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Matrix C( 6);
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// constraint matrix A, column by column
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A[ 0].push_back( 1); A[ 1].push_back( 1); A[ 2].push_back( 1);
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A[ 0].push_back( 0); A[ 1].push_back( 0); A[ 2].push_back( 0);
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A[ 3].push_back( 0); A[ 4].push_back( 0); A[ 5].push_back( 0);
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A[ 3].push_back( 1); A[ 4].push_back( 1); A[ 5].push_back( 1);
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// point matrix C, column by column
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C[ 0].push_back( 2); C[ 1].push_back( 6); C[ 2].push_back( 5);
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C[ 0].push_back( 2); C[ 1].push_back( 1); C[ 2].push_back( 5);
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C[ 3].push_back( -7); C[ 4].push_back( -9); C[ 5].push_back( -6);
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C[ 3].push_back( -4); C[ 4].push_back( -7); C[ 5].push_back( -7);
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// configure partial filtered pricing strategy with default template
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// parameters
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CGAL::QP_partial_filtered_pricing<QPSolverTraits> strategy;
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// solve qp with (explicit) partial filtered pricing strategy
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Solver qp( 6, 2, Vector_iterator( A.begin()),
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QPSolverTraits::B_iterator(1.0),
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QPSolverTraits::C_iterator(0.0),
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QPSolverTraits::D_iterator(C.begin()),
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QPSolverTraits::Row_type_iterator(QPSolverTraits::EQUAL),
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strategy);
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// query solution, if qp is optimal
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if (qp.status() == Solver::OPTIMAL) {
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Solver::Variable_value_iterator v_it;
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Solver::Variable_value_iterator e_it = qp.variables_value_end();
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for (int i = 0, v_it = qp.variables_value_begin(); v_it != e_it; ++v_it, ++i) {
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std::cout << "x[" << i << "]= " << *v_it << std::endl;
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}
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}
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return 0;
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}
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