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# 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)) >= 9.999999995e10:
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
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