def test_lia_qe_requiring_modulus(self):
x = Symbol("x", INT)
y = Symbol("y", INT)
f = Exists([x], Equals(y, Times(x, Int(2))))
with self.assertRaises(ConvertExpressionError):
qelim(f)
try:
qelim(f)
except ConvertExpressionError as ex:
# The modulus operator must be there
self.assertIn("%2", str(ex.expression))
def test_lia_qe_requiring_modulus(self):
x = Symbol("x", INT)
y = Symbol("y", INT)
f = Exists([x], Equals(y, Times(x, Int(2))))
with self.assertRaises(ConvertExpressionError):
qelim(f)
try:
qelim(f)
except ConvertExpressionError as ex:
# The modulus operator must be there
self.assertIn("%2", str(ex.expression))
def test_examples_solving(self):
for example in get_example_formulae():
if example.logic != pysmt.logics.BOOL:
continue
fv = example.expr.get_free_variables()
f = Exists(fv, example.expr)
g = qelim(f, solver_name="shannon").simplify()
if example.is_sat:
self.assertTrue(g.is_true())
else:
self.assertTrue(g.is_false())
f = ForAll(fv, example.expr)
g = qelim(f, solver_name="shannon").simplify()
if example.is_valid:
self.assertTrue(g.is_true())
else:
self.assertTrue(g.is_false())
def test_examples_solving(self):
for example in get_example_formulae():
if example.logic != pysmt.logics.BOOL:
continue
fv = example.expr.get_free_variables()
f = Exists(fv, example.expr)
g = qelim(f, solver_name="shannon").simplify()
if example.is_sat:
self.assertTrue(g.is_true())
else:
self.assertTrue(g.is_false())
f = ForAll(fv, example.expr)
g = qelim(f, solver_name="shannon").simplify()
if example.is_valid:
self.assertTrue(g.is_true())
else:
self.assertTrue(g.is_false())
def test_w_theory(self):
for example in get_example_formulae():
f = example.expr
if example.logic.quantifier_free: continue
try:
res = qelim(f, solver_name="shannon")
self.assertIsNotNone(res, f)
except NoSolverAvailableError:
self.assertTrue(example.logic > pysmt.logics.BOOL, example)
except InternalSolverError:
self.assertTrue(example.logic > pysmt.logics.BOOL, example)
def test_w_theory(self):
for example in get_example_formulae():
f = example.expr
if example.logic.quantifier_free: continue
try:
res = qelim(f, solver_name="shannon")
self.assertIsNotNone(res, f)
except NoSolverAvailableError:
self.assertTrue(example.logic > pysmt.logics.BOOL, example)
except InternalSolverError:
self.assertTrue(example.logic > pysmt.logics.BOOL, example)
from pysmt.shortcuts import Symbol, Or, ForAll, GE, LT, Real, Plus
from pysmt.shortcuts import qelim, is_sat
from pysmt.typing import REAL
x, y, z = [Symbol(s, REAL) for s in "xyz"]
f = ForAll([x], Or(LT(x, Real(5.0)), GE(Plus(x, y, z), Real((17, 2)))))
print("f := %s" % f)
qf_f = qelim(f, solver_name='z3')
print("Quantifier-Free equivalent: %s" % qf_f)
#Quantifier-Free equivalent: (7/2 <= (z + y))
res = is_sat(qf_f, solver_name="msat")
print("SAT check using MathSAT: %s" % res)
#SAT check using MathSAT: True
def test_multiple(self):
f = ForAll([self.x, self.y], Or(self.x, self.y))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, FALSE())
def test_nested(self):
f = Exists([self.x], ForAll([self.y], Or(self.x, self.y)))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, TRUE())
# coordinate (0,0), the top-right has coordinate (5, 10).
#
rect = (x >= 0.0) & (x <= 5.0) & \
(y >= 0.0) & (y <= 10.0)
# The first expression that we build asks if for any value of x we can
# define a value of y that would satisfy the expression above.
f1 = ForAll([x], Exists([y], rect))
# This is false, because we know that if we pick x=11, the formula
# above cannot be satisfied. Eliminating all the symbols in an
# expression is the same as solving the expression.
#
# The function to perform quantifier elimination is called qelim:
qf_f1 = qelim(f1)
print(qf_f1)
# We can restrict the values that x can adopt by imposing them as
# precondition.
#
# If we perform quantifier elimination on this expression we obtain an
# unsurprising result:
#
f2 = ForAll([x], Exists([y], ((x >= 0.0) & (x <= 4.0)).Implies(rect)))
qf_f2 = qelim(f2)
print(qf_f2)
# The power of quantifier elimination lies in the possibility of
# representing sets of solutions. Lets introduce a new symbol z, that
# represents the grid distance (aka Manhattan distance) of (x,y) from
def test_forall(self):
f = ForAll([self.x], And(self.x, self.y))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, FALSE())
def test_multiple(self):
f = ForAll([self.x, self.y], Or(self.x, self.y))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, FALSE())
def test_exists(self):
f = Exists([self.x], And(self.x, self.y))
for qe in self.qe:
g = qelim(f, solver_name=qe)
g = g.simplify()
self.assertEqual(g, self.y, qe)
def test_exists(self):
f = Exists([self.x], And(self.x, self.y))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, self.y)
def test_forall(self):
f = ForAll([self.x], And(self.x, self.y))
for qe in self.qe:
g = qelim(f, solver_name=qe)
g = g.simplify()
self.assertEqual(g, FALSE(), qe)
def test_quantifier_eliminator(self):
f = Exists([self.x], And(self.x, self.y))
g = qelim(f, solver_name="bdd")
self.assertEqual(g, self.y)
def test_nested(self):
f = Exists([self.x], ForAll([self.y], Or(self.x, self.y)))
for qe in self.qe:
g = qelim(f, solver_name=qe)
g = g.simplify()
self.assertEqual(g, TRUE(), qe)
def test_multiple(self):
f = ForAll([self.x, self.y], Or(self.x, self.y))
for qe in self.qe:
g = qelim(f, solver_name=qe)
g = g.simplify()
self.assertEqual(g, FALSE(), qe)
def test_forall(self):
f = ForAll([self.x], And(self.x, self.y))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, FALSE())
# coordinate (0,0), the top-right has coordinate (5, 10).
#
rect = (x >= 0.0) & (x <= 5.0) & \
(y >= 0.0) & (y <= 10.0)
# The first expression that we build asks if for any value of x we can
# define a value of y that would satisfy the expression above.
f1 = ForAll([x], Exists([y], rect))
# This is false, because we know that if we pick x=11, the formula
# above cannot be satisfied. Eliminating all the symbols in an
# expression is the same as solving the expression.
#
# The function to perform quantifier elimination is called qelim:
qf_f1 = qelim(f1)
print(qf_f1)
# We can restrict the values that x can adopt by imposing them as
# precondition.
#
# If we perform quantifier elimination on this expression we obtain an
# unsurprising result:
#
f2 = ForAll([x], Exists([y], ((x >= 0.0) & (x <= 4.0)).Implies(rect)))
qf_f2 = qelim(f2)
print(qf_f2)
# The power of quantifier elimination lies in the possibility of
# representing sets of solutions. Lets introduce a new symbol z, that
# represents the grid distance (aka Manhattan distance) of (x,y) from
def test_nested(self):
f = Exists([self.x], ForAll([self.y], Or(self.x, self.y)))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, TRUE())
def test_quantifier_eliminator(self):
f = Exists([self.x], And(self.x, self.y))
g = qelim(f, solver_name="bdd")
self.assertEqual(g, self.y)
def test_exists(self):
f = Exists([self.x], And(self.x, self.y))
g = qelim(f, solver_name="shannon")
g = g.simplify()
self.assertEqual(g, self.y)
# This example requires Z3 and MathSAT to be installed (but you can
# replace MathSAT with any other solver for QF_LRA)
#
# This examples shows how to:
# 1. Define Real valued constants using floats and fractions
# 2. Perform quantifier elimination
# 3. Pass results from one solver to another
#
from pysmt.shortcuts import Symbol, Or, ForAll, GE, LT, Real, Plus
from pysmt.shortcuts import qelim, is_sat
from pysmt.typing import REAL
x, y, z = [Symbol(s, REAL) for s in "xyz"]
f = ForAll([x], Or(LT(x, Real(5.0)),
GE(Plus(x, y, z), Real((17,2))))) # (17,2) ~> 17/2
print("f := %s" % f)
#f := (forall x . ((x < 5.0) | (17/2 <= (x + y + z))))
qf_f = qelim(f, solver_name="z3")
print("Quantifier-Free equivalent: %s" % qf_f)
#Quantifier-Free equivalent: (7/2 <= (z + y))
res = is_sat(qf_f, solver_name="msat")
print("SAT check using MathSAT: %s" % res)
#SAT check using MathSAT: True
