def test_unpack_results(self):
"""Test unpack results method.
"""
with self.assertRaises(Exception) as cm:
Problem(Minimize(exp(self.a))).unpack_results("blah", None)
self.assertEqual(str(cm.exception), "Unknown solver.")
prob = Problem(Minimize(exp(self.a)), [self.a == 0])
args = prob.get_problem_data(s.SCS)
results_dict = scs.solve(*args)
prob = Problem(Minimize(exp(self.a)), [self.a == 0])
prob.unpack_results(s.SCS, results_dict)
self.assertAlmostEqual(self.a.value, 0, places=4)
self.assertAlmostEqual(prob.value, 1, places=3)
self.assertAlmostEqual(prob.status, s.OPTIMAL)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
args = prob.get_problem_data(s.ECOS)
results_dict = ecos.solve(*args)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
prob.unpack_results(s.ECOS, results_dict)
self.assertItemsAlmostEqual(self.x.value, [0, 0])
self.assertAlmostEqual(prob.value, 0)
self.assertAlmostEqual(prob.status, s.OPTIMAL)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
args = prob.get_problem_data(s.CVXOPT)
results_dict = cvxopt.solvers.conelp(*args)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
prob.unpack_results(s.CVXOPT, results_dict)
self.assertItemsAlmostEqual(self.x.value, [0, 0])
self.assertAlmostEqual(prob.value, 0)
self.assertAlmostEqual(prob.status, s.OPTIMAL)
def test_unpack_results(self):
"""Test unpack results method.
"""
with self.assertRaises(Exception) as cm:
Problem(Minimize(exp(self.a))).unpack_results("blah", None)
self.assertEqual(str(cm.exception), "Unknown solver.")
prob = Problem(Minimize(exp(self.a)), [self.a == 0])
args = prob.get_problem_data(s.SCS)
results_dict = scs.solve(*args)
prob = Problem(Minimize(exp(self.a)), [self.a == 0])
prob.unpack_results(s.SCS, results_dict)
self.assertAlmostEqual(self.a.value, 0, places=4)
self.assertAlmostEqual(prob.value, 1, places=3)
self.assertAlmostEqual(prob.status, s.OPTIMAL)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
args = prob.get_problem_data(s.ECOS)
results_dict = ecos.solve(*args)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
prob.unpack_results(s.ECOS, results_dict)
self.assertItemsAlmostEqual(self.x.value, [0,0])
self.assertAlmostEqual(prob.value, 0)
self.assertAlmostEqual(prob.status, s.OPTIMAL)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
args = prob.get_problem_data(s.CVXOPT)
results_dict = cvxopt.solvers.conelp(*args)
prob = Problem(Minimize(norm(self.x)), [self.x == 0])
prob.unpack_results(s.CVXOPT, results_dict)
self.assertItemsAlmostEqual(self.x.value, [0,0])
self.assertAlmostEqual(prob.value, 0)
self.assertAlmostEqual(prob.status, s.OPTIMAL)
def test_presolve_constant_constraints(self):
"""Test that the presolver removes constraints with no variables.
"""
x = Variable()
obj = Maximize(sqrt(x))
prob = Problem(obj)
c, G, h, dims, A, b = prob.get_problem_data(s.ECOS)
for row in range(A.shape[0]):
assert A[row, :].nnz > 0
for row in range(G.shape[0]):
assert G[row, :].nnz > 0
def test_presolve_constant_constraints(self):
"""Test that the presolver removes constraints with no variables.
"""
x = Variable()
obj = Maximize(sqrt(x))
prob = Problem(obj)
c, G, h, dims, A, b = prob.get_problem_data(s.ECOS)
for row in range(A.shape[0]):
assert A[row, :].nnz > 0
for row in range(G.shape[0]):
assert G[row, :].nnz > 0
def scs_coniclift(x, constraints):
"""
Return (A, b, K) so that
{x : x satisfies constraints}
can be written as
{x : exists y where A @ [x; y] + b in K}.
Parameters
----------
x: cvxpy.Variable
constraints: list of cvxpy.constraints.constraint.Constraint
Each Constraint object must be DCP-compatible.
Notes
-----
This function DOES NOT work when ``x`` has attributes, like ``PSD=True``,
``diag=True``, ``symmetric=True``, etc...
"""
from cvxpy.problems.problem import Problem
from cvxpy.problems.objective import Minimize
from cvxpy.atoms.affine.sum import sum
prob = Problem(Minimize(sum(x)), constraints)
# ^ The objective value is only used to make sure that "x"
# participates in the problem. So, if constraints is an
# empty list, then the support function is the standard
# support function for R^n.
data, chain, invdata = prob.get_problem_data(solver='SCS')
inv = invdata[-2]
x_offset = inv.var_offsets[x.id]
x_indices = np.arange(x_offset, x_offset + x.size)
A = data['A']
x_selector = np.zeros(shape=(A.shape[1], ), dtype=bool)
x_selector[x_indices] = True
A_x = A[:, x_selector]
A_other = A[:, ~x_selector]
A = -sparse.hstack([A_x, A_other])
b = data['b']
K = data['dims']
return A, b, K
