Beispiel #1
0
    def solve(self, objective, constraints, cached_data,
              warm_start, verbose, solver_opts):
        """Returns the result of the call to the solver.

        Parameters
        ----------
        objective : LinOp
            The canonicalized objective.
        constraints : list
            The list of canonicalized cosntraints.
        cached_data : dict
            A map of solver name to cached problem data.
        warm_start : bool
            Not used.
        verbose : bool
            Should the solver print output?
        solver_opts : dict
            Additional arguments for the solver.

        Returns
        -------
        tuple
            (status, optimal value, primal, equality dual, inequality dual)
        """
        # Import basic modelling tools of cylp
        from cylp.cy import CyClpSimplex
        from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray

        # Get problem data
        data = self.get_problem_data(objective, constraints, cached_data)

        c = data[s.C]
        b = data[s.B]
        A = data[s.A]
        dims = data[s.DIMS]
        data[s.BOOL_IDX] = solver_opts[s.BOOL_IDX]
        data[s.INT_IDX] = solver_opts[s.INT_IDX]

        n = c.shape[0]

        # Problem
        model = CyLPModel()

        # Variables
        x = model.addVariable('x', n)

        # Constraints
        # eq
        model += A[0:dims[s.EQ_DIM], :] * x == CyLPArray(b[0:dims[s.EQ_DIM]])

        # leq
        leq_start = dims[s.EQ_DIM]
        leq_end = dims[s.EQ_DIM] + dims[s.LEQ_DIM]
        model += A[leq_start:leq_end, :] * x <= CyLPArray(b[leq_start:leq_end])

        # Objective
        model.objective = c

        # Convert model
        model = CyClpSimplex(model)

        # No boolean vars available in Cbc -> model as int + restrict to [0,1]
        if self.is_mip(data):
            # Mark integer- and binary-vars as "integer"
            model.setInteger(x[data[s.BOOL_IDX]])
            model.setInteger(x[data[s.INT_IDX]])

            # Restrict binary vars only
            idxs = data[s.BOOL_IDX]
            n_idxs = len(idxs)

            model.setColumnLowerSubset(np.arange(n_idxs, dtype=np.int32),
                                       np.array(idxs, np.int32),
                                       np.zeros(n_idxs))

            model.setColumnUpperSubset(np.arange(n_idxs, dtype=np.int32),
                                       np.array(idxs, np.int32),
                                       np.ones(n_idxs))

        # Verbosity Clp
        if not verbose:
            model.logLevel = 0

        # Build model & solve
        status = None
        if self.is_mip(data):
            # Convert model
            cbcModel = model.getCbcModel()

            # Verbosity Cbc
            if not verbose:
                cbcModel.logLevel = 0

            # cylp: /cylp/cy/CyCbcModel.pyx#L134
            # Call CbcMain. Solve the problem using the same parameters used by
            # CbcSolver. Equivalent to solving the model from the command line
            # using cbc's binary.
            cbcModel.solve()
            status = cbcModel.status
        else:
            # cylp: /cylp/cy/CyClpSimplex.pyx
            # Run CLP's initialSolve. It does a presolve and uses primal or dual
            # Simplex to solve a problem.
            status = model.initialSolve()

        results_dict = {}
        results_dict["status"] = status

        if self.is_mip(data):
            results_dict["x"] = cbcModel.primalVariableSolution['x']
            results_dict["obj_value"] = cbcModel.objectiveValue
        else:
            results_dict["x"] = model.primalVariableSolution['x']
            results_dict["obj_value"] = model.objectiveValue

        return self.format_results(results_dict, data, cached_data)
Beispiel #2
0
    def solve_via_data(self, data, warm_start, verbose, solver_opts, solver_cache=None):
        # Import basic modelling tools of cylp
        from cylp.cy import CyClpSimplex
        from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray

        c = data[s.C]
        b = data[s.B]
        A = data[s.A]
        dims = dims_to_solver_dict(data[s.DIMS])

        n = c.shape[0]

        # Problem
        model = CyLPModel()

        # Variables
        x = model.addVariable('x', n)

        # Constraints
        # eq
        model += A[0:dims[s.EQ_DIM], :] * x == CyLPArray(b[0:dims[s.EQ_DIM]])

        # leq
        leq_start = dims[s.EQ_DIM]
        leq_end = dims[s.EQ_DIM] + dims[s.LEQ_DIM]
        model += A[leq_start:leq_end, :] * x <= CyLPArray(b[leq_start:leq_end])

        # Objective
        model.objective = c

        # Convert model
        model = CyClpSimplex(model)

        # No boolean vars available in Cbc -> model as int + restrict to [0,1]
        if data[s.BOOL_IDX] or data[s.INT_IDX]:
            # Mark integer- and binary-vars as "integer"
            model.setInteger(x[data[s.BOOL_IDX]])
            model.setInteger(x[data[s.INT_IDX]])

            # Restrict binary vars only
            idxs = data[s.BOOL_IDX]
            n_idxs = len(idxs)

            model.setColumnLowerSubset(np.arange(n_idxs, dtype=np.int32),
                                       np.array(idxs, np.int32),
                                       np.zeros(n_idxs))

            model.setColumnUpperSubset(np.arange(n_idxs, dtype=np.int32),
                                       np.array(idxs, np.int32),
                                       np.ones(n_idxs))

        # Verbosity Clp
        if not verbose:
            model.logLevel = 0

        # Build model & solve
        status = None
        if data[s.BOOL_IDX] or data[s.INT_IDX]:
            # Convert model
            cbcModel = model.getCbcModel()
            for key, value in solver_opts.items():
                setattr(cbcModel, key, value)

            # Verbosity Cbc
            if not verbose:
                cbcModel.logLevel = 0

            # cylp: /cylp/cy/CyCbcModel.pyx#L134
            # Call CbcMain. Solve the problem using the same parameters used by
            # CbcSolver. Equivalent to solving the model from the command line
            # using cbc's binary.
            cbcModel.solve()
            status = cbcModel.status
        else:
            # cylp: /cylp/cy/CyClpSimplex.pyx
            # Run CLP's initialSolve. It does a presolve and uses primal or dual
            # Simplex to solve a problem.
            status = model.initialSolve()

        solution = {}
        if data[s.BOOL_IDX] or data[s.INT_IDX]:
            solution["status"] = self.STATUS_MAP_MIP[status]
            solution["primal"] = cbcModel.primalVariableSolution['x']
            solution["value"] = cbcModel.objectiveValue
        else:
            solution["status"] = self.STATUS_MAP_LP[status]
            solution["primal"] = model.primalVariableSolution['x']
            solution["value"] = model.objectiveValue

        return solution