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https://github.com/Z3Prover/z3
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130 lines
4.4 KiB
C++
130 lines
4.4 KiB
C++
/*++
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Copyright (c) 2013 Microsoft Corporation
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Module Name:
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theory_wmaxsat.h
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Abstract:
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Weighted Max-SAT theory plugin.
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Author:
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Nikolaj Bjorner (nbjorner) 2013-11-05
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Notes:
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--*/
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#ifndef THEORY_WMAXSAT_H_
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#define THEORY_WMAXSAT_H_
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#include "smt_theory.h"
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#include "smt_clause.h"
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#include "filter_model_converter.h"
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namespace smt {
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class theory_wmaxsat : public theory {
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struct stats {
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unsigned m_num_blocks;
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void reset() { memset(this, 0, sizeof(*this)); }
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stats() { reset(); }
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};
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filter_model_converter& m_mc;
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mutable unsynch_mpz_manager m_mpz;
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app_ref_vector m_vars; // Auxiliary variables per soft clause
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expr_ref_vector m_fmls; // Formulas per soft clause
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vector<rational> m_rweights; // weights of theory variables.
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scoped_mpz_vector m_zweights;
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scoped_mpz_vector m_old_values;
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svector<theory_var> m_costs; // set of asserted theory variables
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svector<theory_var> m_cost_save; // set of asserted theory variables
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rational m_rcost; // current sum of asserted costs
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rational m_rmin_cost; // current maximal cost assignment.
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scoped_mpz m_zcost; // current sum of asserted costs
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scoped_mpz m_zmin_cost; // current maximal cost assignment.
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bool m_found_optimal;
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u_map<theory_var> m_bool2var; // bool_var -> theory_var
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svector<bool_var> m_var2bool; // theory_var -> bool_var
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bool m_propagate;
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bool m_normalize;
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rational m_den; // lcm of denominators for rational weights.
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svector<bool> m_assigned;
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stats m_stats;
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public:
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theory_wmaxsat(ast_manager& m, filter_model_converter& mc);
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virtual ~theory_wmaxsat();
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void get_assignment(svector<bool>& result);
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virtual void init_search_eh();
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bool_var assert_weighted(expr* fml, rational const& w);
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bool_var register_var(app* var, bool attach);
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rational const& get_min_cost();
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class numeral_trail : public trail<context> {
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typedef scoped_mpz T;
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T & m_value;
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scoped_mpz_vector& m_old_values;
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public:
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numeral_trail(T & value, scoped_mpz_vector& old):
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m_value(value),
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m_old_values(old) {
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old.push_back(value);
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}
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virtual ~numeral_trail() {
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}
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virtual void undo(context & ctx) {
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m_value = m_old_values.back();
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m_old_values.shrink(m_old_values.size() - 1);
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}
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};
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virtual void assign_eh(bool_var v, bool is_true);
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virtual final_check_status final_check_eh();
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virtual bool use_diseqs() const {
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return false;
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}
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virtual bool build_models() const {
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return false;
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}
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void reset_local();
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virtual void reset_eh();
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virtual theory * mk_fresh(context * new_ctx) { return 0; }
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virtual bool internalize_atom(app * atom, bool gate_ctx) { return false; }
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virtual bool internalize_term(app * term) { return false; }
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virtual void new_eq_eh(theory_var v1, theory_var v2) { }
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virtual void new_diseq_eh(theory_var v1, theory_var v2) { }
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virtual void display(std::ostream& out) const {}
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virtual void collect_statistics(::statistics & st) const {
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st.update("wmaxsat num blocks", m_stats.m_num_blocks);
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}
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virtual bool can_propagate() {
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return m_propagate;
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}
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virtual void propagate();
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bool is_optimal() const;
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expr_ref mk_block();
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expr_ref mk_optimal_block(svector<bool_var> const& ws, rational const& weight);
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private:
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void block();
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void normalize();
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class compare_cost {
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theory_wmaxsat& m_th;
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public:
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compare_cost(theory_wmaxsat& t):m_th(t) {}
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bool operator() (theory_var v, theory_var w) const {
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return m_th.m_mpz.gt(m_th.m_zweights[v], m_th.m_zweights[w]);
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}
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};
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};
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};
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#endif
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