def apply(self, problem):
"""See docstring for MatrixStuffing.apply"""
inverse_data = InverseData(problem)
# Form the constraints
extractor = CoeffExtractor(inverse_data)
new_obj, new_var, r = self.stuffed_objective(problem, extractor)
inverse_data.r = r
# Lower equality and inequality to Zero and NonPos.
cons = []
for con in problem.constraints:
if isinstance(con, Equality):
con = lower_equality(con)
elif isinstance(con, Inequality):
con = lower_inequality(con)
cons.append(con)
# Batch expressions together, then split apart.
expr_list = [arg for c in cons for arg in c.args]
problem_data_tensor = extractor.affine(expr_list)
Afull, bfull = canon.get_matrix_and_offset_from_unparameterized_tensor(
problem_data_tensor, new_var.size)
if 0 not in Afull.shape and 0 not in bfull.shape:
Afull = cvxtypes.constant()(Afull)
bfull = cvxtypes.constant()(np.atleast_1d(bfull))
new_cons = []
offset = 0
for orig_con, con in zip(problem.constraints, cons):
arg_list = []
for arg in con.args:
A = Afull[offset:offset + arg.size, :]
b = bfull[offset:offset + arg.size]
arg_list.append(reshape(A @ new_var + b, arg.shape))
offset += arg.size
new_constraint = con.copy(arg_list)
new_cons.append(new_constraint)
inverse_data.constraints = new_cons
inverse_data.minimize = type(problem.objective) == Minimize
new_prob = problems.problem.Problem(Minimize(new_obj), new_cons)
return new_prob, inverse_data
def apply(self, problem):
inverse_data = InverseData(problem)
# Form the constraints
extractor = CoeffExtractor(inverse_data)
new_obj, new_var, r = self.stuffed_objective(problem, extractor)
inverse_data.r = r
# Lower equality and inequality to Zero and NonPos.
cons = []
for con in problem.constraints:
if isinstance(con, Equality):
con = lower_equality(con)
elif isinstance(con, Inequality):
con = lower_inequality(con)
elif isinstance(con, SOC) and con.axis == 1:
con = SOC(con.args[0],
con.args[1].T,
axis=0,
constr_id=con.constr_id)
cons.append(con)
# Batch expressions together, then split apart.
expr_list = [arg for con in cons for arg in con.args]
Afull, bfull = extractor.affine(expr_list)
new_cons = []
offset = 0
for con in cons:
arg_list = []
for arg in con.args:
A = Afull[offset:offset + arg.size, :]
b = bfull[offset:offset + arg.size]
arg_list.append(reshape(A * new_var + b, arg.shape))
offset += arg.size
new_cons.append(con.copy(arg_list))
inverse_data.cons_id_map[con.id] = new_cons[-1].id
# Map of old constraint id to new constraint id.
inverse_data.minimize = type(problem.objective) == Minimize
new_prob = problems.problem.Problem(Minimize(new_obj), new_cons)
return new_prob, inverse_data
def apply(self, problem):
"""Returns a stuffed problem.
The returned problem is a minimization problem in which every
constraint in the problem has affine arguments that are expressed in
the form A @ x + b.
Parameters
----------
problem: The problem to stuff; the arguments of every constraint
must be affine
constraints: A list of constraints, whose arguments are affine
Returns
-------
Problem
The stuffed problem
InverseData
Data for solution retrieval
"""
inverse_data = InverseData(problem)
# Form the constraints
extractor = CoeffExtractor(inverse_data)
new_obj, new_var, r = self.stuffed_objective(problem, extractor)
inverse_data.r = r
# Lower equality and inequality to Zero and NonPos.
cons = []
for con in problem.constraints:
if isinstance(con, Equality):
con = lower_equality(con)
elif isinstance(con, Inequality):
con = lower_inequality(con)
elif isinstance(con, SOC) and con.axis == 1:
con = SOC(con.args[0], con.args[1].T, axis=0,
constr_id=con.constr_id)
cons.append(con)
# Batch expressions together, then split apart.
expr_list = [arg for c in cons for arg in c.args]
Afull, bfull = extractor.affine(expr_list)
if 0 not in Afull.shape and 0 not in bfull.shape:
Afull = cvxtypes.constant()(Afull)
bfull = cvxtypes.constant()(bfull)
new_cons = []
offset = 0
for con in cons:
arg_list = []
for arg in con.args:
A = Afull[offset:offset+arg.size, :]
b = bfull[offset:offset+arg.size]
arg_list.append(reshape(A*new_var + b, arg.shape))
offset += arg.size
new_cons.append(con.copy(arg_list))
# Map old constraint id to new constraint id.
inverse_data.cons_id_map[con.id] = new_cons[-1].id
inverse_data.minimize = type(problem.objective) == Minimize
new_prob = problems.problem.Problem(Minimize(new_obj), new_cons)
return new_prob, inverse_data