def test_sp_relaxation(self):
w = Variable('w')
x = Variable('x')
y = Variable('y')
z = Variable('z')
with SignomialsEnabled():
m = Model(x, [x+y >= w, x+y <= z/2, y <= x, y >= 1], {z: 3, w: 3})
r1 = ConstantsRelaxed(m)
self.assertEqual(len(r1.varkeys), 8)
with self.assertRaises(ValueError):
mr1 = Model(x*r1.relaxvars, r1) # no 'prod'
mr1 = Model(x*r1.relaxvars.prod()**10, r1)
cost1 = mr1.localsolve(verbosity=0)["cost"]
self.assertAlmostEqual(cost1/1024, 1, self.ndig)
m.debug(verbosity=0)
with SignomialsEnabled():
m = Model(x, [x+y >= z, x+y <= z/2, y <= x, y >= 1], {z: 3})
if self.solver != "cvxopt":
m.debug(verbosity=0)
r2 = ConstraintsRelaxed(m)
self.assertEqual(len(r2.varkeys), 7)
mr2 = Model(x*r2.relaxvars.prod()**10, r2)
cost2 = mr2.localsolve(verbosity=0)["cost"]
self.assertAlmostEqual(cost2/1024, 1, self.ndig)
with SignomialsEnabled():
m = Model(x, [x+y >= z, x+y <= z/2, y <= x, y >= 1], {z: 3})
if self.solver != "cvxopt":
m.debug(verbosity=0)
r3 = ConstraintsRelaxedEqually(m)
self.assertEqual(len(r3.varkeys), 4)
mr3 = Model(x*r3.relaxvar**10, r3)
cost3 = mr3.localsolve(verbosity=0)["cost"]
self.assertAlmostEqual(cost3/(32*0.8786796585), 1, self.ndig)
def test_sp_relaxation(self):
w = Variable('w')
x = Variable('x')
y = Variable('y')
z = Variable('z')
with SignomialsEnabled():
m = Model(x, [x+y >= w, x+y <= z/2, y <= x, y >= 1], {z: 3, w: 3})
r1 = ConstantsRelaxed(m)
self.assertEqual(len(r1.varkeys), 8)
mr1 = Model(x*r1.relaxvars.prod()**10, r1)
cost1 = mr1.localsolve(verbosity=0)["cost"]
self.assertAlmostEqual(cost1/1024, 1, self.ndig)
m.debug(verbosity=0)
with SignomialsEnabled():
m = Model(x, [x+y >= z, x+y <= z/2, y <= x, y >= 1], {z: 3})
if self.solver != "cvxopt":
m.debug(verbosity=0)
r2 = ConstraintsRelaxed(m)
self.assertEqual(len(r2.varkeys), 7)
mr2 = Model(x*r2.relaxvars.prod()**10, r2)
cost2 = mr2.localsolve(verbosity=0)["cost"]
self.assertAlmostEqual(cost2/1024, 1, self.ndig)
with SignomialsEnabled():
m = Model(x, [x+y >= z, x+y <= z/2, y <= x, y >= 1], {z: 3})
if self.solver != "cvxopt":
m.debug(verbosity=0)
r3 = ConstraintsRelaxedEqually(m)
self.assertEqual(len(r3.varkeys), 4)
mr3 = Model(x*r3.relaxvar**10, r3)
cost3 = mr3.localsolve(verbosity=0)["cost"]
self.assertAlmostEqual(cost3/(32*0.8786796585), 1, self.ndig)
def test_sp_relaxation(self):
w = Variable("w")
x = Variable("x")
y = Variable("y")
z = Variable("z")
with SignomialsEnabled():
m = Model(x, [x + y >= w, x + y <= z / 2, y <= x, y >= 1], {
z: 3,
w: 3
})
r1 = ConstantsRelaxed(m)
self.assertEqual(len(r1.varkeys), 8)
with self.assertRaises(ValueError):
_ = Model(x * r1.relaxvars, r1) # no "prod"
sp = Model(x * r1.relaxvars.prod()**10, r1).sp(use_pccp=False)
cost = sp.localsolve(verbosity=0, solver=self.solver)["cost"]
self.assertAlmostEqual(cost / 1024, 1, self.ndig)
m.debug(verbosity=0, solver=self.solver)
with SignomialsEnabled():
m = Model(x, [x + y >= z, x + y <= z / 2, y <= x, y >= 1], {z: 3})
m.debug(verbosity=0, solver=self.solver)
r2 = ConstraintsRelaxed(m)
self.assertEqual(len(r2.varkeys), 7)
sp = Model(x * r2.relaxvars.prod()**10, r2).sp(use_pccp=False)
cost = sp.localsolve(verbosity=0, solver=self.solver)["cost"]
self.assertAlmostEqual(cost / 1024, 1, self.ndig)
with SignomialsEnabled():
m = Model(x, [x + y >= z, x + y <= z / 2, y <= x, y >= 1], {z: 3})
m.debug(verbosity=0, solver=self.solver)
r3 = ConstraintsRelaxedEqually(m)
self.assertEqual(len(r3.varkeys), 4)
sp = Model(x * r3.relaxvar**10, r3).sp(use_pccp=False)
cost = sp.localsolve(verbosity=0, solver=self.solver)["cost"]
self.assertAlmostEqual(cost / (32 * 0.8786796585), 1, self.ndig)
"Debug examples"
from gpkit import Variable, Model, units
x = Variable("x", "ft")
x_min = Variable("x_min", 2, "ft")
x_max = Variable("x_max", 1, "ft")
y = Variable("y", "volts")
m = Model(x/y, [x <= x_max, x >= x_min])
m.debug()
print("# Now let's try a model unsolvable with relaxed constants\n")
m2 = Model(x, [x <= units("inch"), x >= units("yard")])
m2.debug()
print("# And one that's only unbounded\n")
# the value of x_min was used up in the previous model!
x_min = Variable("x_min", 2, "ft")
m3 = Model(x/y, [x >= x_min])
m3.debug()
x_min = Variable("x_min", 2, "ft")
m4 = Model(x, [x >= x_min])
m4.debug()
"Debug examples"
from gpkit import Variable, Model, units
x = Variable("x", "ft")
x_min = Variable("x_min", 2, "ft")
x_max = Variable("x_max", 1, "ft")
y = Variable("y", "volts")
m = Model(x/y, [x <= x_max, x >= x_min])
m.debug()
print "# Now let's try a model unsolvable with relaxed constants\n"
Model(x, [x <= units("inch"), x >= units("yard")]).debug()
print "# And one that's only unbounded\n"
# the value of x_min was used up in the previous model!
x_min = Variable("x_min", 2, "ft")
Model(x/y, [x >= x_min]).debug()
"Debug examples"
from gpkit import Variable, Model
x = Variable("x", "ft")
x_min = Variable("x_min", 2, "ft")
x_max = Variable("x_max", 1, "ft")
y = Variable("y", "volts")
m = Model(x/y, [x <= x_max, x >= x_min])
m.debug(verbosity=0)
"Debug examples"
from gpkit import Variable, Model
x = Variable("x", "ft")
x_min = Variable("x_min", 2, "ft")
x_max = Variable("x_max", 1, "ft")
y = Variable("y", "volts")
m = Model(x / y, [x <= x_max, x >= x_min])
m.debug(verbosity=0)
