Source code for pm4py.util.lp.variants.cvxopt_solver

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
import sys

from cvxopt import matrix, solvers


[docs]def apply(c, Aub, bub, Aeq, beq, parameters=None): """ Gets the overall solution of the problem Parameters ------------ c c parameter of the algorithm Aub A_ub parameter of the algorithm bub b_ub parameter of the algorithm Aeq A_eq parameter of the algorithm beq b_eq parameter of the algorithm parameters Possible parameters of the algorithm Returns ------------- sol Solution of the LP problem by the given algorithm """ if parameters is None: parameters = {} solver = parameters["solver"] if "solver" in parameters else None c = matrix(c) Aub = matrix(Aub) bub = matrix(bub) if Aeq is not None: Aeq = matrix(Aeq) if beq is not None: beq = matrix(beq) solvers.options['glpk'] = {} solvers.options['glpk']['LPX_K_MSGLEV'] = 0 solvers.options['glpk']['msg_lev'] = 'GLP_MSG_OFF' solvers.options['glpk']['show_progress'] = False solvers.options['glpk']['presolve'] = "GLP_ON" solvers.options['glpk']['meth'] = "GLP_PRIMAL" solvers.options['msg_lev'] = 'GLP_MSG_OFF' solvers.options['show_progress'] = False if solver: sol = solvers.lp(c, Aub, bub, A=Aeq, b=beq, solver=solver) else: sol = solvers.lp(c, Aub, bub, A=Aeq, b=beq) return sol
[docs]def get_prim_obj_from_sol(sol, parameters=None): """ Gets the primal objective from the solution of the LP problem Parameters ------------- sol Solution of the ILP problem by the given algorithm parameters Possible parameters of the algorithm Returns ------------- prim_obj Primal objective """ if parameters is None: parameters = {} return sol["primal objective"]
[docs]def get_points_from_sol(sol, parameters=None): """ Gets the points from the solution Parameters ------------- sol Solution of the LP problem by the given algorithm parameters Possible parameters of the algorithm Returns ------------- points Point of the solution """ if parameters is None: parameters = {} maximize = parameters["maximize"] if "maximize" in parameters else False return_when_none = parameters["return_when_none"] if "return_when_none" in parameters else False var_corr = parameters["var_corr"] if "var_corr" in parameters else {} if sol and 'x' in sol and sol['x'] is not None: return list(sol['x']) else: if return_when_none: if maximize: return [sys.float_info.max] * len(list(var_corr.keys())) return [sys.float_info.min] * len(list(var_corr.keys()))