def solve_via_data(
self,
data: Dict[str, Any],
warm_start: bool,
verbose: bool,
solver_opts: Dict[str, Any],
solver_cache: Dict = None,
) -> Solution:
"""Returns the result of the call to the solver."""
from google.protobuf import text_format
from ortools.glop import parameters_pb2
from ortools.linear_solver import linear_solver_pb2, pywraplp
response = linear_solver_pb2.MPSolutionResponse()
solver = pywraplp.Solver.CreateSolver('GLOP')
solver.LoadModelFromProto(data[self.MODEL_PROTO])
if verbose:
solver.EnableOutput()
if "parameters_proto" in solver_opts:
proto = solver_opts["parameters_proto"]
if not isinstance(proto, parameters_pb2.GlopParameters):
log.error("parameters_proto must be a GlopParameters")
return {"status": s.SOLVER_ERROR}
proto_str = text_format.MessageToString(proto)
if not solver.SetSolverSpecificParametersAsString(proto_str):
return {"status": s.SOLVER_ERROR}
if "time_limit_sec" in solver_opts:
solver.SetTimeLimit(1000 * float(solver_opts["time_limit_sec"]))
solver.Solve()
solver.FillSolutionResponseProto(response)
solution = {}
solution["value"] = response.objective_value
solution["status"] = self._status_map(response)
has_primal = data["num_vars"] == 0 or len(response.variable_value) > 0
if has_primal:
solution["primal"] = array(response.variable_value)
else:
solution["primal"] = None
has_dual = data["num_constraints"] == 0 or len(response.dual_value) > 0
if has_dual:
solution["dual"] = array(response.dual_value)
else:
solution["dual"] = None
# Make solution status more precise depending on whether a solution is
# present.
if solution["status"] == s.SOLVER_ERROR and has_primal and has_dual:
solution["status"] = s.USER_LIMIT
return solution
def test_proto():
input_proto = linear_solver_pb2.MPModelRequest()
text_format.Merge(text_model, input_proto)
solver = pywraplp.Solver('solveFromProto',
pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
print(input_proto)
# For now, create the model from the proto by parsing the proto
solver.LoadModelFromProto(input_proto.model)
solver.EnableOutput()
solver.Solve()
# Fill solution
solution = linear_solver_pb2.MPSolutionResponse()
solver.FillSolutionResponseProto(solution)
print(solution)
def test_proto(self):
input_proto = linear_solver_pb2.MPModelProto()
text_format.Merge(TEXT_MODEL, input_proto)
solver = pywraplp.Solver('solveFromProto',
pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
# For now, create the model from the proto by parsing the proto
errors = solver.LoadModelFromProto(input_proto)
self.assertFalse(errors)
solver.Solve()
# Fill solution
solution = linear_solver_pb2.MPSolutionResponse()
solver.FillSolutionResponseProto(solution)
self.assertEqual(solution.objective_value, 3.0)
self.assertEqual(solution.variable_value[0], 1.0)
self.assertEqual(solution.variable_value[1], 1.0)
self.assertEqual(solution.best_objective_bound, 3.0)
def testSolveFromProto(self):
solver = pywraplp.Solver('', pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)
solver.LoadSolutionFromProto(linear_solver_pb2.MPSolutionResponse())