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:
# 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
def apply(self, problem):
"""See docstring for MatrixStuffing.apply"""
inverse_data = InverseData(problem)
# Form the constraints
extractor = CoeffExtractor(inverse_data)
params_to_P, params_to_q, flattened_variable = self.stuffed_objective(
problem, extractor)
# 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_ineq_to_nonpos(con)
cons.append(con)
# Reorder constraints to Zero, NonPos.
constr_map = group_constraints(cons)
ordered_cons = constr_map[Zero] + constr_map[NonPos]
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_Ab = extractor.affine(expr_list)
inverse_data.minimize = type(problem.objective) == Minimize
new_prob = ParamQuadProg(params_to_P, params_to_q,
flattened_variable, params_to_Ab,
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)
# 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)
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 = utilities.lower_equality(constraint)
elif type(constraint) == Inequality:
constraint = utilities.lower_inequality(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
def apply(self, problem):
inverse_data = InverseData(problem)
new_obj, new_var = self.stuffed_objective(problem, inverse_data)
# Form the constraints
extractor = CoeffExtractor(inverse_data)
new_cons = []
for con in problem.constraints:
arg_list = []
for arg in con.args:
A, b = extractor.get_coeffs(arg)
arg_list.append(reshape(A * new_var + b, arg.shape))
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):
inverse_data = InverseData(problem)
is_minimize = type(problem.objective) == Minimize
chance_expr, chance_constraints = self.chance_tree(problem.objective.args[0], is_minimize, True, False)
chance_objective = Minimize(chance_expr) if is_minimize else Maximize(chance_expr)
if any([type(atom) == cc.quantile for con in problem.constraints for atom in con.atoms()]):
raise DCPError("Quantile atom may not be nested in constraints.")
new_problem = cc.problem.Problem(chance_objective, problem.constraints + chance_constraints)
return new_problem, inverse_data
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):
inverse_data = InverseData(problem)
canon_objective, canon_constraints = self.canonicalize_tree(
problem.objective)
for constraint in problem.constraints:
# canon_constr is the constraint rexpressed in terms of
# its canonicalized arguments, and aux_constr are the constraints
# generated while canonicalizing the arguments of the original
# constraint
canon_constr, aux_constr = self.canonicalize_tree(constraint)
canon_constraints += aux_constr + [canon_constr]
inverse_data.cons_id_map.update({constraint.id: canon_constr.id})
new_problem = problems.problem.Problem(canon_objective,
canon_constraints)
return new_problem, inverse_data
def stuffed_objective(self, problem, inverse_data):
# We need to copy the problem because we are changing atoms in the
# expression tree
problem_copy = problems.problem.Problem(
Minimize(problem.objective.expr.tree_copy()),
[con.tree_copy() for con in problem.constraints])
inverse_data_of_copy = InverseData(problem_copy)
extractor = CoeffExtractor(inverse_data_of_copy)
# extract to x.T * P * x + q.T * x, store r
P, q, r = extractor.quad_form(problem_copy.objective.expr)
# concatenate all variables in one vector
boolean, integer = extract_mip_idx(problem.variables())
x = Variable(inverse_data.x_length, boolean=boolean, integer=integer)
new_obj = QuadForm(x, P) + q.T * x
inverse_data.r = r
return new_obj, x
def apply(self, problem):
inverse_data = InverseData(problem)
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:
real_constr, imag_constr = self.canonicalize_tree(
constraint, inverse_data.real2imag, leaf_map)
if real_constr is not None:
constrs.append(real_constr)
if imag_constr is not None:
constrs.append(imag_constr)
new_problem = problems.problem.Problem(real_obj,
constrs)
return new_problem, 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
def apply(self, problem):
"""
Construct QP problem data stored in a dictionary.
The QP has the following form
minimize 1/2 x' P x + q' x
subject to A x = b
F x <= g
"""
inverse_data = InverseData(problem)
obj = problem.objective
# quadratic part of objective is x.T * P * x but solvers expect
# 0.5*x.T * P * x.
P = 2 * obj.expr.args[0].args[1].value
q = obj.expr.args[1].args[0].value.flatten()
# Get number of variables
n = problem.size_metrics.num_scalar_variables
# TODO(akshayka): This dependence on ConicSolver is hacky; something
# should change here.
eq_cons = [c for c in problem.constraints if type(c) == Zero]
if eq_cons:
eq_coeffs = list(
zip(*[
ConicSolver.get_coeff_offset(con.expr) for con in eq_cons
]))
A = sp.vstack(eq_coeffs[0])
b = -np.concatenate(eq_coeffs[1])
else:
A, b = sp.csr_matrix((0, n)), -np.array([])
ineq_cons = [c for c in problem.constraints if type(c) == NonPos]
if ineq_cons:
ineq_coeffs = list(
zip(*[
ConicSolver.get_coeff_offset(con.expr) for con in ineq_cons
]))
F = sp.vstack(ineq_coeffs[0])
g = -np.concatenate(ineq_coeffs[1])
else:
F, g = sp.csr_matrix((0, n)), -np.array([])
# Create dictionary with problem data
variables = problem.variables()[0]
data = {}
data[s.P] = sp.csc_matrix(P)
data[s.Q] = q
data[s.A] = sp.csc_matrix(A)
data[s.B] = b
data[s.F] = sp.csc_matrix(F)
data[s.G] = g
data[s.BOOL_IDX] = [t[0] for t in variables.boolean_idx]
data[s.INT_IDX] = [t[0] for t in variables.integer_idx]
data['n_var'] = n
data['n_eq'] = A.shape[0]
data['n_ineq'] = F.shape[0]
inverse_data.sorted_constraints = eq_cons + ineq_cons
# Add information about integer variables
inverse_data.is_mip = \
len(data[s.BOOL_IDX]) > 0 or len(data[s.INT_IDX]) > 0
return data, inverse_data
