def test_logical(self):
numpy_compare.assert_equal(sym.logical_not(x == 0), "!((x == 0))")
# Test single-operand logical statements
numpy_compare.assert_equal(sym.logical_and(x >= 1), "(x >= 1)")
numpy_compare.assert_equal(sym.logical_or(x >= 1), "(x >= 1)")
# Test binary operand logical statements
numpy_compare.assert_equal(sym.logical_and(x >= 1, x <= 2),
"((x >= 1) and (x <= 2))")
numpy_compare.assert_equal(sym.logical_or(x <= 1, x >= 2),
"((x >= 2) or (x <= 1))")
# Test multiple operand logical statements
numpy_compare.assert_equal(sym.logical_and(x >= 1, x <= 2, y == 2),
"((y == 2) and (x >= 1) and (x <= 2))")
numpy_compare.assert_equal(sym.logical_or(x >= 1, x <= 2, y == 2),
"((y == 2) or (x >= 1) or (x <= 2))")
def test_logical(self):
self.assertEqual(str(sym.logical_not(x == 0)), "!((x == 0))")
# Test single-operand logical statements
self.assertEqual(str(sym.logical_and(x >= 1)), "(x >= 1)")
self.assertEqual(str(sym.logical_or(x >= 1)), "(x >= 1)")
# Test binary operand logical statements
self.assertEqual(str(sym.logical_and(x >= 1, x <= 2)),
"((x >= 1) and (x <= 2))")
self.assertEqual(str(sym.logical_or(x <= 1, x >= 2)),
"((x >= 2) or (x <= 1))")
# Test multiple operand logical statements
self.assertEqual(str(sym.logical_and(x >= 1, x <= 2, y == 2)),
"((y == 2) and (x >= 1) and (x <= 2))")
self.assertEqual(str(sym.logical_or(x >= 1, x <= 2, y == 2)),
"((y == 2) or (x >= 1) or (x <= 2))")
def test_dreal_satisfiability(self):
x = Variable("x")
f = logical_and(x > 1, x < 2)
interval_box = EqualToDict(
DrealSolver.CheckSatisfiability(f=f, delta=0.01))
self.assertEqual(len(interval_box), 1)
self.assertIsInstance(interval_box[x], DrealSolver.Interval)
def test_logical(self):
self.assertEqual(str(sym.logical_not(x == 0)),
"!((x == 0))")
# Test single-operand logical statements
self.assertEqual(str(sym.logical_and(x >= 1)), "(x >= 1)")
self.assertEqual(str(sym.logical_or(x >= 1)), "(x >= 1)")
# Test binary operand logical statements
self.assertEqual(str(sym.logical_and(x >= 1, x <= 2)),
"((x >= 1) and (x <= 2))")
self.assertEqual(str(sym.logical_or(x <= 1, x >= 2)),
"((x >= 2) or (x <= 1))")
# Test multiple operand logical statements
self.assertEqual(str(sym.logical_and(x >= 1, x <= 2, y == 2)),
"((y == 2) and (x >= 1) and (x <= 2))")
self.assertEqual(str(sym.logical_or(x >= 1, x <= 2, y == 2)),
"((y == 2) or (x >= 1) or (x <= 2))")
def test_minimize(self):
x = Variable("x")
delta = 0.01
interval_box = EqualToDict(
DrealSolver.Minimize(
objective=x**2,
constraint=logical_and(x >= 1, x <= 10),
delta=delta,
local_optimization=DrealSolver.LocalOptimization.kUse))
self.assertEqual(len(interval_box), 1)
self.assertIsInstance(interval_box[x], DrealSolver.Interval)
print(interval_box[x].diam())
self.assertAlmostEqual(interval_box[x].mid(), 1.0, delta=delta)
def __init__(self):
# Create a simple QP that uses all deduced linear constraint types,
# along with a quadratic and linear cost.
# The solution should be [1, 1].
prog = mp.MathematicalProgram()
x = prog.NewContinuousVariables(2, "x")
self.prog = prog
self.x = x
self.constraints = [
# Bounding box
prog.AddLinearConstraint(x[0] >= 1),
# Bounding box
prog.AddLinearConstraint(sym.logical_and(x[1] >= 1, x[1] <= 2.)),
# Linear inequality
prog.AddLinearConstraint(3 * x[0] - x[1] <= 2),
# Linaer equality
prog.AddLinearConstraint(x[0] + 2 * x[1] == 3)]
# TODO(eric.cousineau): Add constant terms
self.costs = [prog.AddLinearCost(x[0] + x[1]),
prog.AddQuadraticCost(0.5 * (x[0]**2 + x[1]**2))]
def __init__(self):
# Create a simple QP that uses all deduced linear constraint types,
# along with a quadratic and linear cost.
# The solution should be [1, 1].
prog = mp.MathematicalProgram()
x = prog.NewContinuousVariables(2, "x")
self.prog = prog
self.x = x
self.constraints = [
# Bounding box
prog.AddLinearConstraint(x[0] >= 1),
# Bounding box
prog.AddLinearConstraint(sym.logical_and(x[1] >= 1, x[1] <= 2.)),
# Linear inequality
prog.AddLinearConstraint(3 * x[0] - x[1] <= 2),
# Linear equality
prog.AddLinearConstraint(x[0] + 2 * x[1] == 3)]
# TODO(eric.cousineau): Add constant terms
self.costs = [prog.AddLinearCost(x[0] + x[1]),
prog.AddQuadraticCost(0.5 * (x[0]**2 + x[1]**2))]
