def apply(self, problem):
"""Returns a new problem and data for inverting the new solution.
Returns
-------
tuple
(dict of arguments needed for the solver, inverse data)
"""
data = {}
inv_data = {self.VAR_ID: problem.variables()[0].id}
data[s.C], data[s.OFFSET] = ConicSolver.get_coeff_offset(
problem.objective.args[0])
data[s.C] = data[s.C].ravel()
inv_data[s.OFFSET] = data[s.OFFSET][0]
constr_map = group_constraints(problem.constraints)
data[ConicSolver.DIMS] = ConeDims(constr_map)
inv_data[self.EQ_CONSTR] = constr_map[Zero]
data[s.A], data[s.B] = self.group_coeff_offset(
problem, constr_map[Zero], ECOS.EXP_CONE_ORDER)
# Order and group nonlinear constraints.
neq_constr = constr_map[NonPos] + constr_map[SOC] + constr_map[ExpCone]
inv_data[self.NEQ_CONSTR] = neq_constr
data[s.G], data[s.H] = self.group_coeff_offset(
problem, neq_constr, ECOS.EXP_CONE_ORDER)
return data, inv_data
def apply(self, problem):
"""Returns a new problem and data for inverting the new solution.
Returns
-------
tuple
(dict of arguments needed for the solver, inverse data)
"""
data = {}
objective, _ = problem.objective.canonical_form
constraints = [con for c in problem.constraints for con in c.canonical_form[1]]
data["objective"] = objective
data["constraints"] = constraints
data[ConicSolver.DIMS] = ConeDims(
group_constraints(problem.constraints))
variables = problem.variables()[0]
data[s.BOOL_IDX] = [t[0] for t in variables.boolean_idx]
data[s.INT_IDX] = [t[0] for t in variables.integer_idx]
inv_data = {self.VAR_ID: problem.variables()[0].id}
# Order and group constraints.
eq_constr = [c for c in problem.constraints if type(c) == Zero]
inv_data[CVXOPT.EQ_CONSTR] = eq_constr
leq_constr = [c for c in problem.constraints if type(c) == NonPos]
soc_constr = [c for c in problem.constraints if type(c) == SOC]
sdp_constr = [c for c in problem.constraints if type(c) == PSD]
exp_constr = [c for c in problem.constraints if type(c) == ExpCone]
inv_data[CVXOPT.NEQ_CONSTR] = leq_constr + soc_constr + sdp_constr + exp_constr
return data, inv_data
def apply(self, problem):
"""Returns a new problem and data for inverting the new solution.
Returns
-------
tuple
(dict of arguments needed for the solver, inverse data)
"""
data = {}
inv_data = {self.VAR_ID: problem.variables()[0].id}
# Parse the coefficient vector from the objective.
data[s.C], data[s.OFFSET] = self.get_coeff_offset(
problem.objective.args[0])
data[s.C] = data[s.C].ravel()
inv_data[s.OFFSET] = data[s.OFFSET][0]
# Order and group nonlinear constraints.
constr_map = group_constraints(problem.constraints)
data[ConicSolver.DIMS] = ConeDims(constr_map)
inv_data[ConicSolver.DIMS] = data[ConicSolver.DIMS]
# SCS requires constraints to be specified in the following order:
# 1. zero cone
# 2. non-negative orthant
# 3. soc
# 4. psd
# 5. exponential
zero_constr = constr_map[Zero]
neq_constr = (constr_map[NonPos] + constr_map[SOC] + constr_map[PSD] +
constr_map[ExpCone])
inv_data[SCS.EQ_CONSTR] = zero_constr
inv_data[SCS.NEQ_CONSTR] = neq_constr
# Obtain A, b such that Ax + s = b, s \in cones.
#
# Note that scs mandates that the cones MUST be ordered with
# zero cones first, then non-nonnegative orthant, then SOC,
# then PSD, then exponential.
data[s.A], data[s.B] = self.group_coeff_offset(
problem, zero_constr + neq_constr, self.EXP_CONE_ORDER)
return data, inv_data