int main() {
// print seed
std::cout << "Random seed: " << rd.get_seed() << std::endl;
// options
CGAL::Quadratic_program_options options;
options.set_auto_validation(true);
// generate a set of small random qp's
for (int i=0; i<tries; ++i) {
int Ax[] = {random_signed(), random_signed()};
int Ay[] = {random_signed(), random_signed()};
int* A[] = {Ax, Ay};
int b[] = {random_signed(), random_signed()};
CGAL::Comparison_result
r[] = {random_rel(), random_rel()};
bool fl[] = {rd.get_bool(), rd.get_bool()};
int l[] = {random_signed(),random_signed()};
bool fu[] = {rd.get_bool(), rd.get_bool()};
int u[] = {random_signed(),random_signed()};
// make sure that l<=u
if (l[0] > u[0]) {int h = l[0]; l[0] = u[0]; u[0] = h;}
if (l[1] > u[1]) {int h = l[1]; l[1] = u[1]; u[1] = h;}
int D1[] = {random_unsigned()};
int D2[] = {0, random_unsigned()};
// can still change D_21 as long as D remains positive-semidefinite
if (D1[0] < D2[1])
D2[0] = rd.get_int(-D1[0], D1[0]+1);
else
D2[0] = rd.get_int(-D2[1], D2[1]+1);
assert(D1[0] * D2[1] >= D2[0] * D2[0]);
int* D[] = {D1, D2};
int c[] = {random_signed(), random_signed()};
int c0 = random_signed();
// now construct the quadratic program; the first two parameters are
// the number of variables and the number of constraints (rows of A)
Program qp (2, 2, A, b, r, fl, l, fu, u, D, c, c0);
// write/read it and check equality
std::stringstream inout;
CGAL::print_quadratic_program (inout, qp);
CGAL::Quadratic_program_from_mps<int> qp2 (inout);
assert(CGAL::QP_functions_detail::are_equal_qp (qp, qp2));
// solve it
Solution s = CGAL::solve_quadratic_program (qp, ET(), options);
assert(s.is_valid());
statistics (s, qp_optimal, qp_infeasible, qp_unbounded);
// also solve it as nqp, lp, nlp
s = CGAL::solve_nonnegative_quadratic_program (qp, ET(), options);
assert(s.is_valid());
statistics (s, nqp_optimal, nqp_infeasible, nqp_unbounded);
s = CGAL::solve_linear_program (qp, ET(), options);
assert(s.is_valid());
statistics (s, lp_optimal, lp_infeasible, lp_unbounded);
s = CGAL::solve_nonnegative_linear_program (qp, ET(), options);
assert(s.is_valid());
statistics (s, nlp_optimal, nlp_infeasible, nlp_unbounded);
}
// output statistics
std::cout << "Solved " << tries
<< " random QP / NQP / LP / NLP .\n"
<< " Optimal: "
<< qp_optimal << " / "
<< nqp_optimal << " / "
<< lp_optimal << " / "
<< nlp_optimal << "\n"
<< " Infeasible: "
<< qp_infeasible << " / "
<< nqp_infeasible << " / "
<< lp_infeasible << " / "
<< nlp_infeasible << "\n"
<< " Unbounded: "
<< qp_unbounded << " / "
<< nqp_unbounded << " / "
<< lp_unbounded << " / "
<< nlp_unbounded << std::endl;
return 0;
}
int main() {
// print seed
std::cout << "Random seed: " << rd.get_seed() << std::endl;
// options
CGAL::Quadratic_program_options options;
options.set_auto_validation(true);
// generate a set of small random qp's
for (int i=0; i<tries; ++i) {
// first choose dimensions
int n = rd.get_int(1,max_dim);
int m = rd.get_int(1,max_dim);
// construct matrix D as C^T C, for C randomly chosen with n columns
int k = rd.get_int (1, 2*n); // number of rows of C
std::vector<std::vector<int> > C (k, std::vector<int>(n, 0));
for (int j=0; j<k+n; ++j) // sparse C
C[rd.get_int(0, k)][rd.get_int(0,n)] =
rd.get_int(-max_entry, max_entry);
// now fill the program
Program p;
// A
for (int j=0; j<n+m; ++j)
p.set_a (rd.get_int(0,n), rd.get_int(0,m), rd.get_double());
// b, r
for (int i=0; i<m/2; ++i) {
p.set_b (rd.get_int(0,m), rd.get_double());
p.set_r (rd.get_int(0,m), random_rel());
}
// fl, l, fu, u
for (int j=0; j<n; ++j) {
double l = rd.get_double();
double u = rd.get_double();
if (l > u) std::swap (l, u);
p.set_l(j, rd.get_bool(), l);
p.set_u(j, rd.get_bool(), u);
}
// D
for (int i=0; i<n; ++i)
for (int j=0; j<=i; ++j) {
double entry = 0;
for (int l=0; l<k; ++l)
entry += C[l][i] * C[l][j];
p.set_d(i, j, entry);
}
// c
for (int j=0; j<n/2; ++j)
p.set_c (rd.get_int(0, n), rd.get_double());
// c0
p.set_c0(rd.get_double());
// solve it
Solution s = CGAL::solve_quadratic_program (p, ET(), options);
assert(s.is_valid());
statistics (s, qp_optimal, qp_infeasible, qp_unbounded);
// also solve it as nqp, lp, nlp
s = CGAL::solve_nonnegative_quadratic_program (p, ET(), options);
assert(s.is_valid());
statistics (s, nqp_optimal, nqp_infeasible, nqp_unbounded);
s = CGAL::solve_linear_program (p, ET(), options);
assert(s.is_valid());
statistics (s, lp_optimal, lp_infeasible, lp_unbounded);
s = CGAL::solve_nonnegative_linear_program (p, ET(), options);
assert(s.is_valid());
statistics (s, nlp_optimal, nlp_infeasible, nlp_unbounded);
}
// output statistics
std::cout << "Solved " << tries
<< " random QP / NQP / LP / NLP .\n"
<< " Optimal: "
<< qp_optimal << " / "
<< nqp_optimal << " / "
<< lp_optimal << " / "
<< nlp_optimal << "\n"
<< " Infeasible: "
<< qp_infeasible << " / "
<< nqp_infeasible << " / "
<< lp_infeasible << " / "
<< nlp_infeasible << "\n"
<< " Unbounded: "
<< qp_unbounded << " / "
<< nqp_unbounded << " / "
<< lp_unbounded << " / "
<< nlp_unbounded << std::endl;
return 0;
}
