def test_pf_matrix_completion(self):
X = cp.Variable((3, 3), pos=True)
obj = cp.Minimize(cp.pf_eigenvalue(X))
known_indices = tuple(zip(*[[0, 0], [0, 2], [1, 1], [2, 0], [2, 1]]))
known_values = np.array([1.0, 1.9, 0.8, 3.2, 5.9])
constr = [
X[known_indices] == known_values,
X[0, 1] * X[1, 0] * X[1, 2] * X[2, 2] == 1.0,
]
problem = cp.Problem(obj, constr)
# smoke test.
problem.solve(SOLVER, gp=True)
optimal_value = problem.value
param = cp.Parameter(shape=known_values.shape,
pos=True,
value=0.5 * known_values)
constr = [
X[known_indices] == param,
X[0, 1] * X[1, 0] * X[1, 2] * X[2, 2] == 1.0,
]
problem = cp.Problem(obj, constr)
problem.solve(SOLVER, gp=True, enforce_dpp=True)
# now change param to point to known_value, and check we recover
# the correct optimal value
param.value = known_values
problem.solve(SOLVER, gp=True, enforce_dpp=True)
self.assertAlmostEqual(problem.value, optimal_value)
def test_pf_matrix_completion(self):
X = cvxpy.Variable((3, 3), pos=True)
obj = cvxpy.Minimize(cvxpy.pf_eigenvalue(X))
known_indices = tuple(zip(*[[0, 0], [0, 2], [1, 1], [2, 0], [2, 1]]))
constr = [
X[known_indices] == [1.0, 1.9, 0.8, 3.2, 5.9],
X[0, 1] * X[1, 0] * X[1, 2] * X[2, 2] == 1.0,
]
problem = cvxpy.Problem(obj, constr)
# smoke test.
problem.solve(SOLVER, gp=True)