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add convex interior generalizer

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Signed-off-by: Nikolaj Bjorner <nbjorner@microsoft.com>
This commit is contained in:
Nikolaj Bjorner 2013-06-10 10:51:56 -04:00
parent 455618bb2b
commit 0210156bf0
4 changed files with 192 additions and 0 deletions
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1
src/muz_qe/fixedpoint_params.pyg
View file

@ -48,6 +48,7 @@ def_module_params('fixedpoint',
('use_multicore_generalizer', BOOL, False, "PDR: extract multiple cores for blocking states"),
('use_inductive_generalizer', BOOL, True, "PDR: generalize lemmas using induction strengthening"),
('use_arith_inductive_generalizer', BOOL, False, "PDR: generalize lemmas using arithmetic heuristics for induction strengthening"),
('use_convex_hull_generalizer', BOOL, False, "PDR: generalize using convex hulls of lemmas"),
('cache_mode', UINT, 0, "PDR: use no (0), symbolic (1) or explicit cache (2) for model search"),
('inductive_reachability_check', BOOL, False, "PDR: assume negation of the cube on the previous level when "
"checking for reachability (not only during cube weakening)"),

3
src/muz_qe/pdr_context.cpp
View file

@ -1572,6 +1572,9 @@ namespace pdr {
}
}
if (m_params.use_convex_hull_generalizer()) {
m_core_generalizers.push_back(alloc(core_convex_hull_generalizer, *this));
}
if (!use_mc && m_params.use_inductive_generalizer()) {
m_core_generalizers.push_back(alloc(core_bool_inductive_generalizer, *this, 0));
}

171
src/muz_qe/pdr_generalizers.cpp
View file

@ -147,6 +147,177 @@ namespace pdr {
}
core_convex_hull_generalizer::core_convex_hull_generalizer(context& ctx):
core_generalizer(ctx),
m(ctx.get_manager()),
a(m),
m_sigma(m),
m_trail(m) {
m_sigma.push_back(m.mk_fresh_const("sigma", a.mk_real()));
m_sigma.push_back(m.mk_fresh_const("sigma", a.mk_real()));
}
void core_convex_hull_generalizer::operator()(model_node& n, expr_ref_vector& core, bool& uses_level) {
manager& pm = n.pt().get_pdr_manager();
expr_ref_vector conv1(m), conv2(m), core1(m), core2(m), eqs(m);
if (core.empty()) {
return;
}
if (!m_left.contains(n.pt().head())) {
expr_ref left(m), right(m);
m_left.insert(n.pt().head(), 0);
unsigned sz = n.pt().sig_size();
for (unsigned i = 0; i < sz; ++i) {
func_decl* fn0 = n.pt().sig(i);
sort* srt = fn0->get_range();
if (a.is_int_real(srt)) {
func_decl* fn1 = pm.o2n(fn0, 0);
left = m.mk_fresh_const(fn1->get_name().str().c_str(), srt);
right = m.mk_fresh_const(fn1->get_name().str().c_str(), srt);
m_left.insert(fn1, left);
m_right.insert(fn1, right);
m_trail.push_back(left);
m_trail.push_back(right);
}
}
}
unsigned sz = n.pt().sig_size();
for (unsigned i = 0; i < sz; ++i) {
expr* left, *right;
func_decl* fn0 = n.pt().sig(i);
func_decl* fn1 = pm.o2n(fn0, 0);
if (m_left.find(fn1, left) && m_right.find(fn1, right)) {
eqs.push_back(m.mk_eq(m.mk_const(fn1), a.mk_add(left, right)));
}
}
if (!mk_convex(core, 0, conv1)) {
IF_VERBOSE(0, verbose_stream() << "Non-convex: " << mk_pp(pm.mk_and(core), m) << "\n";);
return;
}
conv1.append(eqs);
conv1.push_back(a.mk_gt(m_sigma[0].get(), a.mk_numeral(rational(0), a.mk_real())));
conv1.push_back(a.mk_gt(m_sigma[1].get(), a.mk_numeral(rational(0), a.mk_real())));
conv1.push_back(m.mk_eq(a.mk_numeral(rational(1), a.mk_real()), a.mk_add(m_sigma[0].get(), m_sigma[1].get())));
expr_ref fml = n.pt().get_formulas(n.level(), false);
expr_ref_vector fmls(m);
datalog::flatten_and(fml, fmls);
for (unsigned i = 0; i < fmls.size(); ++i) {
fml = m.mk_not(fmls[i].get());
core2.reset();
datalog::flatten_and(fml, core2);
if (!mk_convex(core2, 1, conv2)) {
IF_VERBOSE(0, verbose_stream() << "Non-convex: " << mk_pp(pm.mk_and(core2), m) << "\n";);
continue;
}
conv2.append(conv1);
expr_ref state = pm.mk_and(conv2);
TRACE("pdr", tout << "Check:\n" << mk_pp(state, m) << "\n";
tout << "New formula:\n" << mk_pp(pm.mk_and(core), m) << "\n";
tout << "Old formula:\n" << mk_pp(fml, m) << "\n";
);
model_node nd(0, state, n.pt(), n.level());
if (l_false == n.pt().is_reachable(nd, &conv2, uses_level)) {
TRACE("pdr",
tout << mk_pp(state, m) << "\n";
tout << "Generalized to:\n" << mk_pp(pm.mk_and(conv2), m) << "\n";);
IF_VERBOSE(0,
verbose_stream() << mk_pp(state, m) << "\n";
verbose_stream() << "Generalized to:\n" << mk_pp(pm.mk_and(conv2), m) << "\n";);
core.reset();
core.append(conv2);
}
}
}
bool core_convex_hull_generalizer::mk_convex(expr_ref_vector const& core, unsigned index, expr_ref_vector& conv) {
conv.reset();
for (unsigned i = 0; i < core.size(); ++i) {
mk_convex(core[i], index, conv);
}
return !conv.empty();
}
void core_convex_hull_generalizer::mk_convex(expr* fml, unsigned index, expr_ref_vector& conv) {
expr_ref result(m), r1(m), r2(m);
expr* e1, *e2;
bool is_not = m.is_not(fml, fml);
if (a.is_le(fml, e1, e2) && mk_convex(e1, index, false, r1) && mk_convex(e2, index, false, r2)) {
result = a.mk_le(r1, r2);
}
else if (a.is_ge(fml, e1, e2) && mk_convex(e1, index, false, r1) && mk_convex(e2, index, false, r2)) {
result = a.mk_ge(r1, r2);
}
else if (a.is_gt(fml, e1, e2) && mk_convex(e1, index, false, r1) && mk_convex(e2, index, false, r2)) {
result = a.mk_gt(r1, r2);
}
else if (a.is_lt(fml, e1, e2) && mk_convex(e1, index, false, r1) && mk_convex(e2, index, false, r2)) {
result = a.mk_lt(r1, r2);
}
else if (m.is_eq(fml, e1, e2) && a.is_int_real(e1) && mk_convex(e1, index, false, r1) && mk_convex(e2, index, false, r2)) {
result = m.mk_eq(r1, r2);
}
else {
TRACE("pdr", tout << "Did not handle " << mk_pp(fml, m) << "\n";);
return;
}
if (is_not) {
result = m.mk_not(result);
}
conv.push_back(result);
}
bool core_convex_hull_generalizer::translate(func_decl* f, unsigned index, expr_ref& result) {
expr* tmp;
if (index == 0 && m_left.find(f, tmp)) {
result = tmp;
return true;
}
if (index == 1 && m_right.find(f, tmp)) {
result = tmp;
return true;
}
return false;
}
bool core_convex_hull_generalizer::mk_convex(expr* term, unsigned index, bool is_mul, expr_ref& result) {
if (!is_app(term)) {
return false;
}
app* app = to_app(term);
expr* e1, *e2;
expr_ref r1(m), r2(m);
if (translate(app->get_decl(), index, result)) {
return true;
}
if (a.is_add(term, e1, e2) && mk_convex(e1, index, is_mul, r1) && mk_convex(e2, index, is_mul, r2)) {
result = a.mk_add(r1, r2);
return true;
}
if (a.is_sub(term, e1, e2) && mk_convex(e1, index, is_mul, r1) && mk_convex(e2, index, is_mul, r2)) {
result = a.mk_sub(r1, r2);
return true;
}
if (a.is_mul(term, e1, e2) && mk_convex(e1, index, true, r1) && mk_convex(e2, index, true, r2)) {
result = a.mk_mul(r1, r2);
return true;
}
if (a.is_numeral(term)) {
if (is_mul) {
result = term;
}
else {
result = a.mk_mul(m_sigma[index].get(), term);
}
return true;
}
IF_VERBOSE(0, verbose_stream() << "Not handled: " << mk_pp(term, m) << "\n";);
return false;
}
// ---------------------------------
// core_arith_inductive_generalizer
// NB. this is trying out some ideas for generalization in

17
src/muz_qe/pdr_generalizers.h
View file

@ -73,6 +73,23 @@ namespace pdr {
virtual void collect_statistics(statistics& st) const;
};
class core_convex_hull_generalizer : public core_generalizer {
ast_manager& m;
arith_util a;
expr_ref_vector m_sigma;
expr_ref_vector m_trail;
obj_map<func_decl, expr*> m_left;
obj_map<func_decl, expr*> m_right;
bool mk_convex(expr_ref_vector const& core, unsigned index, expr_ref_vector& conv);
void mk_convex(expr* fml, unsigned index, expr_ref_vector& conv);
bool mk_convex(expr* term, unsigned index, bool is_mul, expr_ref& result);
bool translate(func_decl* fn, unsigned index, expr_ref& result);
public:
core_convex_hull_generalizer(context& ctx);
virtual ~core_convex_hull_generalizer() {}
virtual void operator()(model_node& n, expr_ref_vector& core, bool& uses_level);
};
class core_multi_generalizer : public core_generalizer {
core_bool_inductive_generalizer m_gen;
public: