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https://github.com/Z3Prover/z3
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working on mss/mus v2
Signed-off-by: Nikolaj Bjorner <nbjorner@microsoft.com>
This commit is contained in:
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1b9529e1e1
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@ -61,6 +61,7 @@ Notes:
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#include "mss.h"
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#include "inc_sat_solver.h"
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#include "opt_context.h"
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#include "pb_decl_plugin.h"
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using namespace opt;
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@ -85,6 +86,7 @@ private:
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rational m_max_upper;
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bool m_hill_climb;
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bool m_all_cores;
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bool m_add_upper_bound_block;
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public:
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maxres(context& c,
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@ -97,7 +99,8 @@ public:
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m_trail(m),
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m_st(st),
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m_hill_climb(true),
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m_all_cores(false)
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m_all_cores(false),
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m_add_upper_bound_block(false)
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{
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}
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@ -281,13 +284,12 @@ public:
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*/
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lbool mus_mss2_solver() {
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m_all_cores = true;
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m_add_upper_bound_block = true;
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init();
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init_local();
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sls();
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m_all_cores = true;
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NOT_IMPLEMENTED_YET();
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vector<ptr_vector<expr> > cores;
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return l_undef;
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lbool is_sat = l_true;
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while (m_lower < m_upper && is_sat == l_true) {
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TRACE("opt",
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@ -318,15 +320,21 @@ public:
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break;
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}
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SASSERT(is_sat == l_true);
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// there is some best model,
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// TBD: there is some best model,
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// retrieve it from the get_cores calls.
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// extend it to a maximal assignment
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// extracting the mss and mcs.
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set_mus(false);
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ptr_vector<expr> mss, mcs;
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is_sat = m_mss(cores, mss, mcs);
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set_mus(true);
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if (is_sat != l_true) return is_sat;
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//
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model_ref mdl;
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m_mss.get_model(mdl); // last model is best way to reduce search space.
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update_assignment(mdl.get());
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is_sat = process_sat(mcs);
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}
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m_lower = m_upper;
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return l_true;
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@ -446,14 +454,9 @@ public:
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lbool process_sat(ptr_vector<expr> const& corr_set) {
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expr_ref fml(m), tmp(m);
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TRACE("opt", display_vec(tout << "corr_set: ", corr_set.size(), corr_set.c_ptr()););
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if (corr_set.empty()) {
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return l_true;
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}
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remove_core(corr_set);
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rational w = split_core(corr_set);
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TRACE("opt", display_vec(tout << " corr_set: ", corr_set.size(), corr_set.c_ptr()););
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cs_max_resolve(corr_set, w);
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cs_max_resolve(corr_set, w);
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return l_true;
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}
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@ -479,6 +482,7 @@ public:
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lbool process_unsat(ptr_vector<expr> const& core) {
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expr_ref fml(m);
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remove_core(core);
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SASSERT(!core.empty());
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rational w = split_core(core);
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TRACE("opt", display_vec(tout << "minimized core: ", core.size(), core.c_ptr()););
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max_resolve(core, w);
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@ -524,9 +528,8 @@ public:
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}
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rational split_core(ptr_vector<expr> const& core) {
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if (core.empty()) return rational(0);
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// find the minimal weight:
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SASSERT(!core.empty());
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rational w = get_weight(core[0]);
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for (unsigned i = 1; i < core.size(); ++i) {
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rational w2 = get_weight(core[i]);
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@ -602,8 +605,8 @@ public:
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// cs is a correction set (a complement of a (maximal) satisfying assignment).
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void cs_max_resolve(ptr_vector<expr> const& cs, rational const& w) {
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if (cs.empty()) return;
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TRACE("opt", display_vec(tout << "correction set: ", cs.size(), cs.c_ptr()););
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SASSERT(!cs.empty());
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expr_ref fml(m), asum(m);
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app_ref cls(m), d(m), dd(m);
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m_B.reset();
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@ -737,6 +740,17 @@ public:
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// verify_assignment();
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IF_VERBOSE(1, verbose_stream() <<
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"(opt.maxres [" << m_lower << ":" << m_upper << "])\n";);
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if (m_add_upper_bound_block) {
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pb_util u(m);
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expr_ref_vector nsoft(m);
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expr_ref fml(m);
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for (unsigned i = 0; i < m_soft.size(); ++i) {
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nsoft.push_back(m.mk_not(m_soft[i].get()));
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}
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fml = u.mk_lt(nsoft.size(), m_weights.c_ptr(), nsoft.c_ptr(), m_upper);
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s().assert_expr(fml);
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}
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}
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void remove_soft(ptr_vector<expr> const& core, expr_ref_vector& asms) {
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@ -22,7 +22,6 @@ Notes:
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#include "smt_literal.h"
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#include "mus.h"
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#include "ast_pp.h"
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#include "model_smt2_pp.h"
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using namespace opt;
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@ -34,9 +33,13 @@ struct mus::imp {
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expr_ref_vector m_cls2expr;
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obj_map<expr, unsigned> m_expr2cls;
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volatile bool m_cancel;
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model_ref m_model;
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expr_ref_vector m_soft;
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vector<rational> m_weights;
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rational m_weight;
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imp(solver& s, ast_manager& m):
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m_s(s), m(m), m_cls2expr(m), m_cancel(false)
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m_s(s), m(m), m_cls2expr(m), m_cancel(false), m_soft(m)
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{}
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void reset() {
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@ -100,6 +103,7 @@ struct mus::imp {
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case l_true:
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assumptions.push_back(cls);
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mus.push_back(cls_id);
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update_model();
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break;
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default:
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core_exprs.reset();
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@ -145,6 +149,40 @@ struct mus::imp {
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out << "\n";
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}
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void set_soft(unsigned sz, expr* const* soft, rational const* weights) {
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m_model.reset();
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m_weight.reset();
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m_soft.append(sz, soft);
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m_weights.append(sz, weights);
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for (unsigned i = 0; i < sz; ++i) {
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m_weight += weights[i];
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}
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}
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void update_model() {
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if (m_soft.empty()) return;
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model_ref mdl;
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expr_ref tmp(m);
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m_s.get_model(mdl);
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rational w;
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for (unsigned i = 0; i < m_soft.size(); ++i) {
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mdl->eval(m_soft[i].get(), tmp);
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if (!m.is_true(tmp)) {
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w += m_weights[i];
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}
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}
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if (w < m_weight || !m_model.get()) {
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m_model = mdl;
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m_weight = w;
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}
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}
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rational get_best_model(model_ref& mdl) {
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mdl = m_model;
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return m_weight;
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}
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};
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mus::mus(solver& s, ast_manager& m) {
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@ -170,3 +208,11 @@ void mus::set_cancel(bool f) {
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void mus::reset() {
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m_imp->reset();
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}
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void mus::set_soft(unsigned sz, expr* const* soft, rational const* weights) {
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m_imp->set_soft(sz, soft, weights);
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}
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rational mus::get_best_model(model_ref& mdl) {
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return m_imp->get_best_model(mdl);
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}
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@ -40,6 +40,16 @@ namespace opt {
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void reset();
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void set_cancel(bool f);
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/**
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Instrument MUS extraction to also provide the minimal
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penalty model, if any is found.
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The minimal penalty model has the least weight for the
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supplied soft constraints.
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*/
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void set_soft(unsigned sz, expr* const* soft, rational const* weights);
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rational get_best_model(model_ref& mdl);
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};
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};
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