# PuLP : Python LP Modeler # Version 1.4.2 # Copyright (c) 2002-2005, Jean-Sebastien Roy (js@jeannot.org) # Modifications Copyright (c) 2007- Stuart Anthony Mitchell (s.mitchell@auckland.ac.nz) # $Id:solvers.py 1791 2008-04-23 22:54:34Z smit023 $ # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" from .core import LpSolver_CMD, subprocess, PulpSolverError import os from .. import constants import warnings class MIPCL_CMD(LpSolver_CMD): """The MIPCL_CMD solver""" name = 'MIPCL_CMD' def __init__(self, path=None, keepFiles=False, mip=True, msg=True, options=None, timeLimit=None): """ :param bool mip: if False, assume LP even if integer variables :param bool msg: if False, no log is shown :param float timeLimit: maximum time for solver (in seconds) :param list options: list of additional options to pass to solver :param bool keepFiles: if True, files are saved in the current directory and not deleted after solving :param str path: path to the solver binary """ LpSolver_CMD.__init__(self, mip=mip, msg=msg, timeLimit=timeLimit, options=options, path=path, keepFiles=keepFiles) def defaultPath(self): return self.executableExtension("mps_mipcl") def available(self): """True if the solver is available""" return self.executable(self.path) def actualSolve(self, lp): """Solve a well formulated lp problem""" if not self.executable(self.path): raise PulpSolverError("PuLP: cannot execute " + self.path) tmpMps, tmpSol = self.create_tmp_files(lp.name, 'mps', 'sol') if lp.sense == constants.LpMaximize: # we swap the objectives # because it does not handle maximization. warnings.warn('MIPCL_CMD does not allow maximization, ' 'we will minimize the inverse of the objective function.') lp += -lp.objective lp.checkDuplicateVars() lp.checkLengthVars(52) lp.writeMPS(tmpMps, mpsSense=lp.sense) # just to report duplicated variables: try: os.remove(tmpSol) except: pass cmd = self.path cmd += ' %s' % tmpMps cmd += ' -solfile %s' % tmpSol if self.timeLimit is not None: cmd += ' -time %s' % self.timeLimit for option in self.options: cmd += ' ' + option if lp.isMIP(): if not self.mip: warnings.warn("MIPCL_CMD cannot solve the relaxation of a problem") if self.msg: pipe = None else: pipe = open(os.devnull, 'w') return_code = subprocess.call(cmd.split(), stdout=pipe, stderr=pipe) # We need to undo the objective swap before finishing if lp.sense == constants.LpMaximize: lp += -lp.objective if return_code != 0: raise PulpSolverError("PuLP: Error while trying to execute "+self.path) if not os.path.exists(tmpSol): status = constants.LpStatusNotSolved status_sol = constants.LpSolutionNoSolutionFound values = None else: status, values, status_sol = self.readsol(tmpSol) self.delete_tmp_files(tmpMps, tmpSol) lp.assignStatus(status, status_sol) if status not in [constants.LpStatusInfeasible, constants.LpStatusNotSolved]: lp.assignVarsVals(values) return status @staticmethod def readsol(filename): """Read a MIPCL solution file""" with open(filename) as f: content = f.readlines() content = [l.strip() for l in content] values = {} if not len(content): return constants.LpStatusNotSolved, values, constants.LpSolutionNoSolutionFound first_line = content[0] if first_line == '=infeas=': return constants.LpStatusInfeasible, values, constants.LpSolutionInfeasible objective, value = first_line.split() # this is a workaround. # Not sure if it always returns this limit when unbounded. if abs(float(value)) >= 9.999999995e+10: return constants.LpStatusUnbounded, values, constants.LpSolutionUnbounded for line in content[1:]: name, value = line.split() values[name] = float(value) # I'm not sure how this solver announces the optimality # of a solution so we assume it is integer feasible return constants.LpStatusOptimal, values, constants.LpSolutionIntegerFeasible