def test_psd_constraints(self):
""" Test positive semi-definite constraints
"""
C = Variable((3, 3))
obj = Maximize(C[0, 2])
constraints = [
diag(C) == 1, C[0, 1] == 0.6, C[1, 2] == -0.3, C == C.T, C >> 0
]
prob = Problem(obj, constraints)
self.assertTrue(FlipObjective().accepts(prob))
p_min = FlipObjective().apply(prob)
self.assertTrue(ConeMatrixStuffing().accepts(p_min[0]))
C = Variable((2, 2))
obj = Maximize(C[0, 1])
constraints = [C == 1, C >> [[2, 0], [0, 2]]]
prob = Problem(obj, constraints)
self.assertTrue(FlipObjective().accepts(prob))
p_min = FlipObjective().apply(prob)
self.assertTrue(ConeMatrixStuffing().accepts(p_min[0]))
C = Variable((2, 2), symmetric=True)
obj = Minimize(C[0, 0])
constraints = [C << [[2, 0], [0, 2]]]
prob, _ = CvxAttr2Constr().apply(Problem(obj, constraints))
self.assertTrue(ConeMatrixStuffing().accepts(prob))
def apply(self, problem):
if not attributes_present(problem.variables(), CONVEX_ATTRIBUTES):
return problem, ()
# For each unique variable, add constraints.
id2new_var = {}
id2new_obj = {}
id2old_var = {}
constr = []
for var in problem.variables():
if var.id not in id2new_var:
id2old_var[var.id] = var
new_var = False
new_attr = var.attributes.copy()
for key in CONVEX_ATTRIBUTES:
if new_attr[key]:
new_var = True
new_attr[key] = False
if attributes_present([var], SYMMETRIC_ATTRIBUTES):
n = var.shape[0]
shape = (n*(n+1)//2, 1)
upper_tri = Variable(shape, var_id=var.id, **new_attr)
upper_tri.set_variable_of_provenance(var)
id2new_var[var.id] = upper_tri
fill_coeff = Constant(upper_tri_to_full(n))
full_mat = fill_coeff @ upper_tri
obj = reshape(full_mat, (n, n))
elif var.attributes['diag']:
diag_var = Variable(var.shape[0], var_id=var.id, **new_attr)
diag_var.set_variable_of_provenance(var)
id2new_var[var.id] = diag_var
obj = diag(diag_var)
elif new_var:
obj = Variable(var.shape, var_id=var.id, **new_attr)
obj.set_variable_of_provenance(var)
id2new_var[var.id] = obj
else:
obj = var
id2new_var[var.id] = obj
id2new_obj[id(var)] = obj
if var.is_pos() or var.is_nonneg():
constr.append(obj >= 0)
elif var.is_neg() or var.is_nonpos():
constr.append(obj <= 0)
elif var.is_psd():
constr.append(obj >> 0)
elif var.attributes['NSD']:
constr.append(obj << 0)
# Create new problem.
obj = problem.objective.tree_copy(id_objects=id2new_obj)
cons_id_map = {}
for cons in problem.constraints:
constr.append(cons.tree_copy(id_objects=id2new_obj))
cons_id_map[cons.id] = constr[-1].id
inverse_data = (id2new_var, id2old_var, cons_id_map)
return cvxtypes.problem()(obj, constr), inverse_data