def accepts(self, problem):
return (type(problem.objective) == Minimize
and problem.objective.is_quadratic() and problem.is_dcp()
and not convex_attributes(problem.variables()) and all(
type(c) == Zero or type(c) == NonPos
for c in problem.constraints)
and are_args_affine(problem.constraints))
def accepts(problem):
return (type(problem.objective) == Minimize
and problem.objective.is_quadratic() and problem.is_dcp()
and not convex_attributes(problem.variables()) and all(
type(c) in [Zero, NonPos, Equality, Inequality]
for c in problem.constraints)
and are_args_affine(problem.constraints) and problem.is_dpp())
def accepts(self, problem):
return (isinstance(problem, ParamConeProg)
and (self.MIP_CAPABLE or not problem.is_mixed_integer())
and not convex_attributes([problem.x])
and (len(problem.constraints) > 0 or not self.REQUIRES_CONSTR)
and all(type(c) in self.SUPPORTED_CONSTRAINTS for c in
problem.constraints))
def accepts(self, problem):
return (type(problem.objective) == Minimize
and (self.MIP_CAPABLE or not problem.is_mixed_integer())
and is_stuffed_cone_objective(problem.objective)
and not convex_attributes(problem.variables())
and (len(problem.constraints) > 0 or not self.REQUIRES_CONSTR)
and all(type(c) in self.SUPPORTED_CONSTRAINTS for c in
problem.constraints)
and all(is_stuffed_cone_constraint(c) for c in
problem.constraints))
def accepts(problem):
"""
Problems with quadratic, piecewise affine objectives,
piecewise-linear constraints inequality constraints, and
affine equality constraints are accepted by the reduction.
"""
return (problem.objective.expr.is_qpwa() and not set(
['PSD', 'NSD']).intersection(convex_attributes(problem.variables()))
and all((type(c) in (Inequality, NonPos) and c.expr.is_pwl()) or
(type(c) in (Equality, Zero) and are_args_affine([c]))
for c in problem.constraints))
def accepts(self, problem):
"""
Problems with quadratic, piecewise affine objectives,
piecewise-linear constraints inequality constraints, and
affine equality constraints are accepted.
"""
return (((type(problem.objective) == Minimize
and problem.objective.expr.is_qpwa())
or problem.objective.expr.is_affine())
and not set(['PSD', 'NSD']).intersection(
convex_attributes(problem.variables())) and all(
(type(c) == NonPos and c.args[0].is_pwl()) or
(type(c) == Zero and are_args_affine([c]))
for c in problem.constraints))
def accepts(self, problem):
return (type(problem.objective) == Minimize
and problem.objective.expr.is_affine()
and not convex_attributes(problem.variables())
and are_args_affine(problem.constraints))
