def value(self):
if getattr(self, 'problem', None) is None:
return None
try:
return self.problem.problem.solution.get_objective_value() + getattr(self.problem, "_objective_offset", 0)
except CplexSolverError as err:
raise SolverError(str(err))
def _get_constraint_values(self):
if len(self.problem.primals) == 0:
raise SolverError("The problem has not been solved yet!")
constraint_primals = [
self.problem.cprimals[c.name] for c in self._constraints
]
return constraint_primals
def _get_shadow_prices(self):
if self.is_integer:
raise ValueError("Dual values are not well-defined for integer problems")
try:
return self.problem.solution.get_dual_values()
except CplexSolverError as err:
raise SolverError(str(err))
def primal(self):
if self.problem is None:
return None
try:
# return self._round_primal_to_bounds(primal_from_solver) # Test assertions fail
return self.problem.problem.solution.get_activity_levels(self.name)
except CplexSolverError as err:
raise SolverError(str(err))
def shadow_prices(self):
if self.is_integer:
raise ValueError("Dual values are not well-defined for integer problems")
try:
return collections.OrderedDict(
zip((constraint.name for constraint in self.constraints), self.problem.solution.get_dual_values()))
except CplexSolverError as err:
raise SolverError(str(err))
def reduced_costs(self):
if self.is_integer:
raise ValueError("Dual values are not well-defined for integer problems")
try:
return collections.OrderedDict(
zip((variable.name for variable in self.variables), self.problem.solution.get_reduced_costs()))
except CplexSolverError as err:
raise SolverError(str(err))
def _optimize(self):
try:
self.problem.solve()
except CplexSolverError as err:
raise SolverError(str(err))
cplex_status = self.problem.solution.get_status()
self._original_status = self.problem.solution.get_status_string()
status = _CPLEX_STATUS_TO_STATUS[cplex_status]
return status
def dual(self):
if self.problem is None:
return None
if self.problem.is_integer:
raise ValueError("Dual values are not well-defined for integer problems")
try:
return self.problem.problem.solution.get_dual_values(self.name)
except CplexSolverError as err:
raise SolverError(str(err))
def primal_values(self):
try:
primal_values = collections.OrderedDict(
(variable.name, variable._round_primal_to_bounds(primal))
for variable, primal in zip(self.variables, self.problem.solution.get_values())
)
except CplexSolverError as err:
raise SolverError(str(err))
return primal_values
def _get_shadow_prices(self):
if len(self.problem.primals) == 0:
raise SolverError("The problem has not been solved yet!")
dual_values = [-self.problem.duals[c.name] for c in self._constraints]
return dual_values
def _get_reduced_costs(self):
if len(self.problem.duals) == 0:
raise SolverError("The problem has not been solved yet!")
reduced_costs = [self.problem.vduals[v.name] for v in self._variables]
return reduced_costs
def _get_primal_values(self):
if len(self.problem.primals) == 0:
raise SolverError("The problem has not been solved yet!")
primal_values = [self.problem.primals[v.name] for v in self._variables]
return primal_values
def _get_constraint_values(self):
try:
return self.problem.solution.get_activity_levels()
except CplexSolverError as err:
raise SolverError(str(err))
def _get_primal_values(self):
try:
primal_values = self.problem.solution.get_values()
except CplexSolverError as err:
raise SolverError(str(err))
return primal_values
def _get_primal(self):
try:
return self.problem.problem.solution.get_values(self.name)
except CplexSolverError as err:
raise SolverError(str(err))
def constraint_values(self):
try:
return collections.OrderedDict(
zip((constraint.name for constraint in self.constraints), self.problem.solution.get_activity_levels()))
except CplexSolverError as err:
raise SolverError(str(err))
