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

'''
    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 blas
from cvxopt import glpk

this_options = {}
this_options["LPX_K_MSGLEV"] = 0
this_options["msg_lev"] = "GLP_MSG_OFF"
this_options["show_progress"] = False
this_options["presolve"] = "GLP_ON"

this_options_lp = {}
this_options_lp["LPX_K_MSGLEV"] = 0
this_options_lp["msg_lev"] = "GLP_MSG_OFF"
this_options_lp["show_progress"] = False
this_options_lp["presolve"] = "GLP_ON"

TOL = 10**(-5)


[docs]def check_lp_sol_is_integer(x): for i in range(len(x)): if abs(x[i] - round(x[i])) > TOL: return False return True
[docs]def custom_solve_ilp(c, G, h, A, b): status, x, y, z = glpk.lp(c, G, h, A, b, options=this_options_lp) if status == "optimal": if not check_lp_sol_is_integer(x): size = G.size[1] I = {i for i in range(size)} status, x = glpk.ilp(c, G, h, A, b, I=I, options=this_options) if status == 'optimal': pcost = blas.dot(c, x) else: pcost = None return {'status': status, 'x': x, 'primal objective': pcost} else: return {'status': status, 'x': None, 'primal objective': None}
[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 """ sol = custom_solve_ilp(c, Aub, bub, Aeq, 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 """ 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()))