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fixes to new solver, add mode for using nlsat solver eagerly from nla_core
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22 changed files with 156 additions and 164 deletions
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@ -25,9 +25,10 @@ Revision History:
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#include <limits>
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#include <iomanip>
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#include <cstring>
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#include "math/lp/lp_utils.h"
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#include "util/stopwatch.h"
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#include "util/statistics.h"
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#include "util/params.h"
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#include "math/lp/lp_utils.h"
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#include "math/lp/lp_types.h"
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namespace lp {
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@ -174,141 +175,93 @@ private:
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random_gen m_rand;
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public:
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void updt_params(params_ref const& p);
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bool enable_hnf() const { return m_enable_hnf; }
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bool& enable_hnf() { return m_enable_hnf; }
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unsigned nlsat_delay() const { return m_nlsat_delay; }
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bool int_run_gcd_test() const { return m_int_run_gcd_test; }
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bool& int_run_gcd_test() { return m_int_run_gcd_test; }
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unsigned reps_in_scaler;
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unsigned reps_in_scaler { 20 };
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// when the absolute value of an element is less than pivot_epsilon
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// in pivoting, we treat it as a zero
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double pivot_epsilon;
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double pivot_epsilon { 0.00000001 };
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// see Chatal, page 115
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double positive_price_epsilon;
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double positive_price_epsilon { 1e-7 };
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// a quotation "if some choice of the entering variable leads to an eta matrix
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// whose diagonal element in the eta column is less than e2 (entering_diag_epsilon) in magnitude, the this choice is rejected ...
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double entering_diag_epsilon;
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int c_partial_pivoting; // this is the constant c from page 410
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unsigned depth_of_rook_search;
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bool using_partial_pivoting;
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double entering_diag_epsilon { 1e-8 };
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int c_partial_pivoting { 10 }; // this is the constant c from page 410
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unsigned depth_of_rook_search { 4 };
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bool using_partial_pivoting { true };
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// dissertation of Achim Koberstein
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// if Bx - b is different at any component more that refactor_epsilon then we refactor
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double refactor_tolerance;
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double pivot_tolerance;
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double zero_tolerance;
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double drop_tolerance;
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double tolerance_for_artificials;
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double can_be_taken_to_basis_tolerance;
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double refactor_tolerance { 1e-4 };
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double pivot_tolerance { 1e-6 };
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double zero_tolerance { 1e-12 };
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double drop_tolerance { 1e-14 };
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double tolerance_for_artificials { 1e-4 };
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double can_be_taken_to_basis_tolerance { 0.00001 };
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unsigned percent_of_entering_to_check; // we try to find a profitable column in a percentage of the columns
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bool use_scaling;
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double scaling_maximum;
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double scaling_minimum;
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double harris_feasibility_tolerance; // page 179 of Istvan Maros
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double ignore_epsilon_of_harris;
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unsigned max_number_of_iterations_with_no_improvements;
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unsigned max_total_number_of_iterations;
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unsigned percent_of_entering_to_check { 5 }; // we try to find a profitable column in a percentage of the columns
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bool use_scaling { true };
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double scaling_maximum { 1.0 };
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double scaling_minimum { 0.5 };
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double harris_feasibility_tolerance { 1e-7 }; // page 179 of Istvan Maros
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double ignore_epsilon_of_harris { 10e-5 };
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unsigned max_number_of_iterations_with_no_improvements { 2000000 };
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unsigned max_total_number_of_iterations { 2000000 };
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double time_limit; // the maximum time limit of the total run time in seconds
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// dual section
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double dual_feasibility_tolerance; // // page 71 of the PhD thesis of Achim Koberstein
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double primal_feasibility_tolerance; // page 71 of the PhD thesis of Achim Koberstein
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double relative_primal_feasibility_tolerance; // page 71 of the PhD thesis of Achim Koberstein
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double dual_feasibility_tolerance { 1e-7 }; // page 71 of the PhD thesis of Achim Koberstein
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double primal_feasibility_tolerance { 1e-7 }; // page 71 of the PhD thesis of Achim Koberstein
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double relative_primal_feasibility_tolerance { 1e-9 }; // page 71 of the PhD thesis of Achim Koberstein
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// end of dual section
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bool m_bound_propagation;
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bool presolve_with_double_solver_for_lar;
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bool m_bound_propagation { true };
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bool presolve_with_double_solver_for_lar { true };
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simplex_strategy_enum m_simplex_strategy;
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int report_frequency;
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bool print_statistics;
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unsigned column_norms_update_frequency;
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bool scale_with_ratio;
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double density_threshold;
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bool use_breakpoints_in_feasibility_search;
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unsigned max_row_length_for_bound_propagation;
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bool backup_costs;
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unsigned column_number_threshold_for_using_lu_in_lar_solver;
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unsigned m_int_gomory_cut_period;
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unsigned m_int_find_cube_period;
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int report_frequency { 1000 };
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bool print_statistics { false };
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unsigned column_norms_update_frequency { 12000 };
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bool scale_with_ratio { true };
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double density_threshold { 0.7 };
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bool use_breakpoints_in_feasibility_search { false };
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unsigned max_row_length_for_bound_propagation { 300 };
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bool backup_costs { true };
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unsigned column_number_threshold_for_using_lu_in_lar_solver { 4000 };
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unsigned m_int_gomory_cut_period { 4 };
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unsigned m_int_find_cube_period { 4 };
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private:
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unsigned m_hnf_cut_period;
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bool m_int_run_gcd_test;
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unsigned m_hnf_cut_period { 4 };
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bool m_int_run_gcd_test { true };
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public:
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unsigned limit_on_rows_for_hnf_cutter;
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unsigned limit_on_columns_for_hnf_cutter;
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unsigned limit_on_rows_for_hnf_cutter { 75 };
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unsigned limit_on_columns_for_hnf_cutter { 150 };
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private:
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bool m_enable_hnf;
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bool m_print_external_var_name;
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bool m_cheap_eqs;
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unsigned m_nlsat_delay;
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bool m_enable_hnf { true };
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bool m_print_external_var_name { false };
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bool m_cheap_eqs { false };
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public:
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bool print_external_var_name() const { return m_print_external_var_name; }
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bool& print_external_var_name() { return m_print_external_var_name; }
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bool cheap_eqs() const { return m_cheap_eqs;}
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bool& cheap_eqs() { return m_cheap_eqs;}
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unsigned hnf_cut_period() const { return m_hnf_cut_period; }
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void set_hnf_cut_period(unsigned period) { m_hnf_cut_period = period; }
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unsigned random_next() { return m_rand(); }
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void set_random_seed(unsigned s) { m_rand.set_seed(s); }
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bool bound_progation() const {
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bool bound_progation() const {
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return m_bound_propagation;
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}
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bool& bound_propagation() {
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return m_bound_propagation;
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}
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bool& bound_propagation() { return m_bound_propagation; }
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lp_settings() : m_default_resource_limit(*this),
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m_resource_limit(&m_default_resource_limit),
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m_debug_out(&std::cout),
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m_message_out(&std::cout),
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reps_in_scaler(20),
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pivot_epsilon(0.00000001),
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positive_price_epsilon(1e-7),
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entering_diag_epsilon (1e-8),
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c_partial_pivoting (10), // this is the constant c from page 410
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depth_of_rook_search (4),
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using_partial_pivoting (true),
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// dissertation of Achim Koberstein
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// if Bx - b is different at any component more that refactor_epsilon then we refactor
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refactor_tolerance ( 1e-4),
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pivot_tolerance ( 1e-6),
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zero_tolerance ( 1e-12),
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drop_tolerance ( 1e-14),
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tolerance_for_artificials ( 1e-4),
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can_be_taken_to_basis_tolerance ( 0.00001),
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percent_of_entering_to_check ( 5),// we try to find a profitable column in a percentage of the columns
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use_scaling ( true),
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scaling_maximum ( 1),
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scaling_minimum ( 0.5),
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harris_feasibility_tolerance ( 1e-7), // page 179 of Istvan Maros
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ignore_epsilon_of_harris ( 10e-5),
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max_number_of_iterations_with_no_improvements ( 2000000),
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max_total_number_of_iterations ( 20000000),
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time_limit ( std::numeric_limits<double>::max()), // the maximum time limit of the total run time in seconds
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// dual section
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dual_feasibility_tolerance ( 1e-7), // // page 71 of the PhD thesis of Achim Koberstein
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primal_feasibility_tolerance ( 1e-7), // page 71 of the PhD thesis of Achim Koberstein
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relative_primal_feasibility_tolerance ( 1e-9), // page 71 of the PhD thesis of Achim Koberstein
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m_bound_propagation ( true),
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presolve_with_double_solver_for_lar(true),
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m_simplex_strategy(simplex_strategy_enum::tableau_rows),
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report_frequency(1000),
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print_statistics(false),
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column_norms_update_frequency(12000),
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scale_with_ratio(true),
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density_threshold(0.7),
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use_breakpoints_in_feasibility_search(false),
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max_row_length_for_bound_propagation(300),
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backup_costs(true),
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column_number_threshold_for_using_lu_in_lar_solver(4000),
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m_int_gomory_cut_period(4),
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m_int_find_cube_period(4),
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m_hnf_cut_period(4),
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m_int_run_gcd_test(true),
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limit_on_rows_for_hnf_cutter(75),
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limit_on_columns_for_hnf_cutter(150),
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m_enable_hnf(true),
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m_print_external_var_name(false)
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m_simplex_strategy(simplex_strategy_enum::tableau_rows)
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{}
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void set_resource_limit(lp_resource_limit& lim) { m_resource_limit = &lim; }
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