def apply(self, problem):
inverse_data = InverseData(problem)
# Form the constraints
extractor = CoeffExtractor(inverse_data)
params_to_objective, flattened_variable = self.stuffed_objective(
problem, extractor)
# Lower equality and inequality to Zero and NonNeg.
cons = []
for con in problem.constraints:
if isinstance(con, Equality):
con = lower_equality(con)
elif isinstance(con, Inequality):
con = lower_ineq_to_nonneg(con)
elif isinstance(con, NonPos):
con = nonpos2nonneg(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)
elif isinstance(con, PowCone3D) and con.args[0].ndim > 1:
x, y, z = con.args
alpha = con.alpha
con = PowCone3D(x.flatten(), y.flatten(), z.flatten(), alpha.flatten(),
constr_id=con.constr_id)
elif isinstance(con, ExpCone) and con.args[0].ndim > 1:
x, y, z = con.args
con = ExpCone(x.flatten(), y.flatten(), z.flatten(),
constr_id=con.constr_id)
cons.append(con)
# Reorder constraints to Zero, NonNeg, SOC, PSD, EXP, PowCone3D
constr_map = group_constraints(cons)
ordered_cons = constr_map[Zero] + constr_map[NonNeg] + \
constr_map[SOC] + constr_map[PSD] + constr_map[ExpCone] + constr_map[PowCone3D]
inverse_data.cons_id_map = {con.id: con.id for con in ordered_cons}
inverse_data.constraints = ordered_cons
# Batch expressions together, then split apart.
expr_list = [arg for c in ordered_cons for arg in c.args]
params_to_problem_data = extractor.affine(expr_list)
inverse_data.minimize = type(problem.objective) == Minimize
new_prob = ParamConeProg(params_to_objective,
flattened_variable,
params_to_problem_data,
problem.variables(),
inverse_data.var_offsets,
ordered_cons,
problem.parameters(),
inverse_data.param_id_map)
return new_prob, inverse_data
def apply(self, problem):
inverse_data = InverseData(problem)
real2imag = {
var.id: lu.get_id()
for var in problem.variables() if var.is_complex()
}
constr_dict = {
cons.id: lu.get_id()
for cons in problem.constraints if cons.is_complex()
}
real2imag.update(constr_dict)
inverse_data.real2imag = real2imag
leaf_map = {}
real_obj, imag_obj = self.canonicalize_tree(problem.objective,
inverse_data.real2imag,
leaf_map)
assert imag_obj is None
constrs = []
for constraint in problem.constraints:
if type(constraint) == Equality:
constraint = lower_equality(constraint)
elif type(constraint) == Inequality:
constraint = lower_ineq_to_nonneg(constraint)
# real2imag maps variable id to a potential new variable
# created for the imaginary part.
real_constrs, imag_constrs = self.canonicalize_tree(
constraint, inverse_data.real2imag, leaf_map)
if real_constrs is not None:
constrs.extend(real_constrs)
if imag_constrs is not None:
constrs.extend(imag_constrs)
new_problem = problems.problem.Problem(real_obj, constrs)
return new_problem, inverse_data