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dev(lp): performance tuning in find_leaving_on_harris_theta

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Signed-off-by: Lev Nachmanson <levnach@microsoft.com>
This commit is contained in:
Lev Nachmanson 2016-01-11 11:07:02 -08:00 committed by Leonardo de Moura
parent d098dfe326
commit 99dcad0dda
4 changed files with 8 additions and 49 deletions
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1
src/util/lp/core_solver_pretty_printer.h
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@ -8,6 +8,7 @@
#pragma once
#include <string>
#include <algorithm>
#include <vector>
namespace lean {
template <typename T, typename X> class lp_core_solver_base; // forward definition

1
src/util/lp/eta_matrix.h
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@ -6,6 +6,7 @@
*/
#pragma once
#include <vector>
namespace lean {
// This is the sum of a unit matrix and a one-column matrix

21
src/util/lp/lp_primal_core_solver.h
View file

@ -148,7 +148,6 @@ public: // todo : move public lower ; it is for debug only
int find_leaving_on_harris_theta(X const & harris_theta, X & t) {
int leaving = -1;
int leaving_fixed = -1; // we would like to get rid of fixed columns in the basis first because they might bring t to zero
T pivot_abs_max;
T pivot_abs_max_fixed;
// we know already that there is no bound flip on entering
@ -167,7 +166,7 @@ public: // todo : move public lower ; it is for debug only
unsigned j = this->m_basis[i];
limit_theta_on_basis_column(j, - this->m_ed[i] * m_sign_of_entering_delta, ratio);
if (ratio <= harris_theta) {
if (get_current_x_is_infeasible() && !m_recalc_reduced_costs) { // when we have made several basic variables feasible then we need to recalculate the costs and the reduced costs: here we are catching this case
if (!m_recalc_reduced_costs && get_current_x_is_infeasible()) { // when we have made several basic variables feasible we need to recalculate the costs and the reduced costs: here we are catching this case
if (!is_zero(this->m_costs[j])) {
if (column_with_non_zero_cost != -1)
m_recalc_reduced_costs = true;
@ -179,28 +178,12 @@ public: // todo : move public lower ; it is for debug only
t = ratio;
leaving = j;
pivot_abs_max = abs(this->m_ed[i]);
if (this->m_column_type[j] == fixed) {
leaving_fixed = j;
pivot_abs_max_fixed = pivot_abs_max;
}
} else if (this->m_column_type[j] == fixed) {
if (leaving_fixed == -1 || abs(this->m_ed[i]) > pivot_abs_max_fixed) {
pivot_abs_max_fixed = abs(this->m_ed[i]);
leaving_fixed = j;
}
}
}
if (++i == this->m_m) i = 0;
} while ( i != initial_i);
restore_harris_eps();
// if (m_j_uht.size() > 1) {
// cout << "under set " << std::endl;
// for(auto j : m_j_uht)
// print_column(j);
// // }
// cout << "done with under set" << std::endl;
return leaving; // debug
// return leaving_fixed != -1 ? leaving_fixed : leaving;
return leaving;
}

34
src/util/lp/lp_primal_simplex.h
View file

@ -230,12 +230,12 @@ public:
solve_with_total_inf();
}
void fill_A_x_and_basis_for_stage_on_total_inf() {
void fill_A_x_and_basis_for_stage_one_total_inf() {
for (unsigned row = 0; row < this->row_count(); row++)
fill_A_x_and_basis_for_stage_on_total_inf_for_row(row);
fill_A_x_and_basis_for_stage_one_total_inf_for_row(row);
}
void fill_A_x_and_basis_for_stage_on_total_inf_for_row(unsigned row) {
void fill_A_x_and_basis_for_stage_one_total_inf_for_row(unsigned row) {
lean_assert(row < this->row_count());
auto ext_row_it = this->m_core_solver_rows_to_external_rows.find(row);
lean_assert(ext_row_it != this->m_core_solver_rows_to_external_rows.end());
@ -279,7 +279,7 @@ public:
this->m_basis.resize(this->row_count());
this->m_costs.clear();
this->m_costs.resize(total_vars, zero_of_type<T>());
fill_A_x_and_basis_for_stage_on_total_inf();
fill_A_x_and_basis_for_stage_one_total_inf();
this->print_statistics_on_A();
this->fill_column_names_for_core_solver();
int j = this->m_A->column_count() - 1;
@ -304,32 +304,6 @@ public:
}
// void stage_one_of_total_inf() {
// std::cout << "starting stage_one_of_total_inf()" << std::endl;
// int total_vars = this->m_A->column_count() + this->row_count();
// m_low_bounds.clear();
// m_low_bounds.resize(total_vars, zero_of_type<X>()); // low bounds are shifted ot zero
// this->m_x.resize(total_vars, numeric_traits<T>::zero());
// this->m_basis.resize(this->row_count());
// this->m_costs.clear();
// this->m_costs.resize(total_vars, zero_of_type<T>());
// fill_A_x_and_basis_for_stage_on_total_inf();
// this->print_statistics_on_A();
// this->fill_column_names_for_core_solver();
// m_core_solver = new lp_primal_core_solver<T, X>(*this->m_A,
// this->m_b,
// this->m_x,
// this->m_basis,
// this->m_costs,
// this->m_column_types,
// m_low_bounds,
// this->m_upper_bounds,
// this->m_settings, this->m_name_map);
// m_core_solver->find_feasible_solution();
// this->m_first_stage_iterations = m_core_solver->m_total_iterations;
// this->m_status = m_core_solver->m_status == lp_status::OPTIMAL? lp_status::FEASIBLE: m_core_solver->m_status; // just map FEASIBLE to OPTIMAL and do not change the rest
// }
~lp_primal_simplex() {
if (m_core_solver != nullptr) {
delete m_core_solver;