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deb-python-pulp/src/pulp/solvers.py

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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."""
"""
This file contains the solver classes for PuLP
Note that the solvers that require a compiled extension may not work in
the current version
"""
import os
import sys
from time import clock
try:
import configparser
except ImportError:
import ConfigParser as configparser
from . import sparse
import collections
import warnings
from tempfile import mktemp
from .constants import *
import logging
log = logging.getLogger(__name__)
if os.name == "posix" and sys.version_info[0] < 3:
try:
import subprocess32 as subprocess
except ImportError:
log.debug("Thread-safe subprocess32 module not found! "
"Using unsafe built-in subprocess module instead.")
import subprocess
else:
import subprocess
class PulpSolverError(PulpError):
"""
Pulp Solver-related exceptions
"""
pass
#import configuration information
def initialize(filename, operating_system='linux', arch='64'):
""" reads the configuration file to initialise the module"""
here = os.path.dirname(filename)
config = configparser.SafeConfigParser({'here':here,
'os':operating_system, 'arch':arch})
config.read(filename)
try:
cplex_dll_path = config.get("locations", "CplexPath")
except configparser.Error:
cplex_dll_path = 'libcplex110.so'
try:
try:
ilm_cplex_license = config.get("licenses",
"ilm_cplex_license").decode("string-escape").replace('"','')
except AttributeError:
ilm_cplex_license = config.get("licenses",
"ilm_cplex_license").replace('"','')
except configparser.Error:
ilm_cplex_license = ''
try:
ilm_cplex_license_signature = config.getint("licenses",
"ilm_cplex_license_signature")
except configparser.Error:
ilm_cplex_license_signature = 0
try:
coinMP_path = config.get("locations", "CoinMPPath").split(', ')
except configparser.Error:
coinMP_path = ['libCoinMP.so']
try:
gurobi_path = config.get("locations", "GurobiPath")
except configparser.Error:
gurobi_path = '/opt/gurobi201/linux32/lib/python2.5'
try:
cbc_path = config.get("locations", "CbcPath")
except configparser.Error:
cbc_path = 'cbc'
try:
glpk_path = config.get("locations", "GlpkPath")
except configparser.Error:
glpk_path = 'glpsol'
try:
pulp_cbc_path = config.get("locations", "PulpCbcPath")
except configparser.Error:
pulp_cbc_path = 'cbc'
for i,path in enumerate(coinMP_path):
if not os.path.dirname(path):
#if no pathname is supplied assume the file is in the same directory
coinMP_path[i] = os.path.join(os.path.dirname(config_filename),path)
return cplex_dll_path, ilm_cplex_license, ilm_cplex_license_signature,\
coinMP_path, gurobi_path, cbc_path, glpk_path, pulp_cbc_path
#pick up the correct config file depending on operating system
PULPCFGFILE = "pulp.cfg"
is_64bits = sys.maxsize > 2**32
if is_64bits:
arch = '64'
else:
arch = '32'
operating_system = None
if sys.platform in ['win32', 'cli']:
operating_system = 'win'
PULPCFGFILE += ".win"
elif sys.platform in ['darwin']:
operating_system = "osx"
arch = '64'
PULPCFGFILE += ".osx"
else:
operating_system = "linux"
PULPCFGFILE += ".linux"
if __name__ != '__main__':
DIRNAME = os.path.dirname(__file__)
config_filename = os.path.join(DIRNAME,
PULPCFGFILE)
else: #run as a script
from .pulp import __file__ as fname
DIRNAME = os.path.dirname(fname)
config_filename = os.path.join(DIRNAME,
PULPCFGFILE)
cplex_dll_path, ilm_cplex_license, ilm_cplex_license_signature, \
coinMP_path, gurobi_path, cbc_path, glpk_path, pulp_cbc_path = \
initialize(config_filename, operating_system, arch)
# See later for LpSolverDefault definition
class LpSolver:
"""A generic LP Solver"""
def __init__(self, mip = True, msg = True, options = [], *args, **kwargs):
self.mip = mip
self.msg = msg
self.options = options
def available(self):
"""True if the solver is available"""
raise NotImplementedError
def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
raise NotImplementedError
def actualResolve(self,lp, **kwargs):
"""
uses existing problem information and solves the problem
If it is not implelemented in the solver
just solve again
"""
self.actualSolve(lp, **kwargs)
def copy(self):
"""Make a copy of self"""
aCopy = self.__class__()
aCopy.mip = self.mip
aCopy.msg = self.msg
aCopy.options = self.options
return aCopy
def solve(self, lp):
"""Solve the problem lp"""
# Always go through the solve method of LpProblem
return lp.solve(self)
#TODO: Not sure if this code should be here or in a child class
def getCplexStyleArrays(self,lp,
senseDict={LpConstraintEQ:"E", LpConstraintLE:"L", LpConstraintGE:"G"},
LpVarCategories = {LpContinuous: "C",LpInteger: "I"},
LpObjSenses = {LpMaximize : -1,
LpMinimize : 1},
infBound = 1e20
):
"""returns the arrays suitable to pass to a cdll Cplex
or other solvers that are similar
Copyright (c) Stuart Mitchell 2007
"""
rangeCount = 0
variables=list(lp.variables())
numVars = len(variables)
#associate each variable with a ordinal
self.v2n=dict(((variables[i],i) for i in range(numVars)))
self.vname2n=dict(((variables[i].name,i) for i in range(numVars)))
self.n2v=dict((i,variables[i]) for i in range(numVars))
#objective values
objSense = LpObjSenses[lp.sense]
NumVarDoubleArray = ctypes.c_double * numVars
objectCoeffs=NumVarDoubleArray()
#print "Get objective Values"
for v,val in lp.objective.items():
objectCoeffs[self.v2n[v]]=val
#values for variables
objectConst = ctypes.c_double(0.0)
NumVarStrArray = ctypes.c_char_p * numVars
colNames = NumVarStrArray()
lowerBounds = NumVarDoubleArray()
upperBounds = NumVarDoubleArray()
initValues = NumVarDoubleArray()
for v in lp.variables():
colNames[self.v2n[v]] = str(v.name)
initValues[self.v2n[v]] = 0.0
if v.lowBound != None:
lowerBounds[self.v2n[v]] = v.lowBound
else:
lowerBounds[self.v2n[v]] = -infBound
if v.upBound != None:
upperBounds[self.v2n[v]] = v.upBound
else:
upperBounds[self.v2n[v]] = infBound
#values for constraints
numRows =len(lp.constraints)
NumRowDoubleArray = ctypes.c_double * numRows
NumRowStrArray = ctypes.c_char_p * numRows
NumRowCharArray = ctypes.c_char * numRows
rhsValues = NumRowDoubleArray()
rangeValues = NumRowDoubleArray()
rowNames = NumRowStrArray()
rowType = NumRowCharArray()
self.c2n = {}
self.n2c = {}
i = 0
for c in lp.constraints:
rhsValues[i] = -lp.constraints[c].constant
#for ranged constraints a<= constraint >=b
rangeValues[i] = 0.0
rowNames[i] = str(c)
rowType[i] = senseDict[lp.constraints[c].sense]
self.c2n[c] = i
self.n2c[i] = c
i = i+1
#return the coefficient matrix as a series of vectors
coeffs = lp.coefficients()
sparseMatrix = sparse.Matrix(list(range(numRows)), list(range(numVars)))
for var,row,coeff in coeffs:
sparseMatrix.add(self.c2n[row], self.vname2n[var], coeff)
(numels, mystartsBase, mylenBase, myindBase,
myelemBase) = sparseMatrix.col_based_arrays()
elemBase = ctypesArrayFill(myelemBase, ctypes.c_double)
indBase = ctypesArrayFill(myindBase, ctypes.c_int)
startsBase = ctypesArrayFill(mystartsBase, ctypes.c_int)
lenBase = ctypesArrayFill(mylenBase, ctypes.c_int)
#MIP Variables
NumVarCharArray = ctypes.c_char * numVars
columnType = NumVarCharArray()
if lp.isMIP():
for v in lp.variables():
columnType[self.v2n[v]] = LpVarCategories[v.cat]
self.addedVars = numVars
self.addedRows = numRows
return (numVars, numRows, numels, rangeCount,
objSense, objectCoeffs, objectConst,
rhsValues, rangeValues, rowType, startsBase, lenBase, indBase,
elemBase, lowerBounds, upperBounds, initValues, colNames,
rowNames, columnType, self.n2v, self.n2c)
class LpSolver_CMD(LpSolver):
"""A generic command line LP Solver"""
def __init__(self, path=None, keepFiles=0, mip=1, msg=1, options=[]):
LpSolver.__init__(self, mip, msg, options)
if path is None:
self.path = self.defaultPath()
else:
self.path = path
self.keepFiles = keepFiles
self.setTmpDir()
def copy(self):
"""Make a copy of self"""
aCopy = LpSolver.copy(self)
aCopy.path = self.path
aCopy.keepFiles = self.keepFiles
aCopy.tmpDir = self.tmpDir
return aCopy
def setTmpDir(self):
"""Set the tmpDir attribute to a reasonnable location for a temporary
directory"""
if os.name != 'nt':
# On unix use /tmp by default
self.tmpDir = os.environ.get("TMPDIR", "/tmp")
self.tmpDir = os.environ.get("TMP", self.tmpDir)
else:
# On Windows use the current directory
self.tmpDir = os.environ.get("TMPDIR", "")
self.tmpDir = os.environ.get("TMP", self.tmpDir)
self.tmpDir = os.environ.get("TEMP", self.tmpDir)
if not os.path.isdir(self.tmpDir):
self.tmpDir = ""
elif not os.access(self.tmpDir, os.F_OK + os.W_OK):
self.tmpDir = ""
def defaultPath(self):
raise NotImplementedError
def executableExtension(name):
if os.name != 'nt':
return name
else:
return name+".exe"
executableExtension = staticmethod(executableExtension)
def executable(command):
"""Checks that the solver command is executable,
And returns the actual path to it."""
if os.path.isabs(command):
if os.path.exists(command) and os.access(command, os.X_OK):
return command
for path in os.environ.get("PATH", []).split(os.pathsep):
new_path = os.path.join(path, command)
if os.path.exists(new_path) and os.access(new_path, os.X_OK):
return os.path.join(path, command)
return False
executable = staticmethod(executable)
class GLPK_CMD(LpSolver_CMD):
"""The GLPK LP solver"""
def defaultPath(self):
return self.executableExtension(glpk_path)
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)
if not self.keepFiles:
pid = os.getpid()
tmpLp = os.path.join(self.tmpDir, "%d-pulp.lp" % pid)
tmpSol = os.path.join(self.tmpDir, "%d-pulp.sol" % pid)
else:
tmpLp = lp.name+"-pulp.lp"
tmpSol = lp.name+"-pulp.sol"
lp.writeLP(tmpLp, writeSOS = 0)
proc = ["glpsol", "--cpxlp", tmpLp, "-o", tmpSol]
if not self.mip: proc.append('--nomip')
proc.extend(self.options)
self.solution_time = clock()
if not self.msg:
proc[0] = self.path
pipe = open(os.devnull, 'w')
rc = subprocess.call(proc, stdout = pipe,
stderr = pipe)
if rc:
raise PulpSolverError("PuLP: Error while trying to execute "+self.path)
else:
if os.name != 'nt':
rc = os.spawnvp(os.P_WAIT, self.path, proc)
else:
rc = os.spawnv(os.P_WAIT, self.executable(self.path), proc)
if rc == 127:
raise PulpSolverError("PuLP: Error while trying to execute "+self.path)
self.solution_time += clock()
if not os.path.exists(tmpSol):
raise PulpSolverError("PuLP: Error while executing "+self.path)
lp.status, values = self.readsol(tmpSol)
lp.assignVarsVals(values)
if not self.keepFiles:
try: os.remove(tmpLp)
except: pass
try: os.remove(tmpSol)
except: pass
return lp.status
def readsol(self,filename):
"""Read a GLPK solution file"""
with open(filename) as f:
f.readline()
rows = int(f.readline().split()[1])
cols = int(f.readline().split()[1])
f.readline()
statusString = f.readline()[12:-1]
glpkStatus = {
"INTEGER OPTIMAL":LpStatusOptimal,
"INTEGER NON-OPTIMAL":LpStatusOptimal,
"OPTIMAL":LpStatusOptimal,
"INFEASIBLE (FINAL)":LpStatusInfeasible,
"INTEGER UNDEFINED":LpStatusUndefined,
"UNBOUNDED":LpStatusUnbounded,
"UNDEFINED":LpStatusUndefined,
"INTEGER EMPTY":LpStatusInfeasible
}
#print "statusString ",statusString
if statusString not in glpkStatus:
raise PulpSolverError("Unknown status returned by GLPK")
status = glpkStatus[statusString]
isInteger = statusString in ["INTEGER NON-OPTIMAL","INTEGER OPTIMAL","INTEGER UNDEFINED"]
values = {}
for i in range(4): f.readline()
for i in range(rows):
line = f.readline().split()
if len(line) ==2: f.readline()
for i in range(3):
f.readline()
for i in range(cols):
line = f.readline().split()
name = line[1]
if len(line) ==2: line = [0,0]+f.readline().split()
if isInteger:
if line[2] == "*": value = int(float(line[3]))
else: value = float(line[2])
else:
value = float(line[3])
values[name] = value
return status, values
GLPK = GLPK_CMD
class CPLEX_CMD(LpSolver_CMD):
"""The CPLEX LP solver"""
def __init__(self, path = None, keepFiles = 0, mip = 1,
msg = 0, options = [], timelimit = None):
LpSolver_CMD.__init__(self, path, keepFiles, mip, msg, options)
self.timelimit = timelimit
def defaultPath(self):
return self.executableExtension("cplex")
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)
if not self.keepFiles:
pid = os.getpid()
tmpLp = os.path.join(self.tmpDir, "%d-pulp.lp" % pid)
tmpSol = os.path.join(self.tmpDir, "%d-pulp.sol" % pid)
else:
tmpLp = lp.name+"-pulp.lp"
tmpSol = lp.name+"-pulp.sol"
lp.writeLP(tmpLp, writeSOS = 1)
try: os.remove(tmpSol)
except: pass
if not self.msg:
cplex = subprocess.Popen(self.path, stdin = subprocess.PIPE,
stdout = subprocess.PIPE, stderr = subprocess.PIPE)
else:
cplex = subprocess.Popen(self.path, stdin = subprocess.PIPE)
cplex_cmds = "read "+tmpLp+"\n"
if self.timelimit is not None:
cplex_cmds += "set timelimit " + str(self.timelimit) + "\n"
for option in self.options:
cplex_cmds += option+"\n"
if lp.isMIP():
if self.mip:
cplex_cmds += "mipopt\n"
cplex_cmds += "change problem fixed\n"
else:
cplex_cmds += "change problem lp\n"
cplex_cmds += "optimize\n"
cplex_cmds += "write "+tmpSol+"\n"
cplex_cmds += "quit\n"
cplex_cmds = cplex_cmds.encode('UTF-8')
cplex.communicate(cplex_cmds)
if cplex.returncode != 0:
raise PulpSolverError("PuLP: Error while trying to execute "+self.path)
if not self.keepFiles:
try: os.remove(tmpLp)
except: pass
if not os.path.exists(tmpSol):
status = LpStatusInfeasible
else:
status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)
if not self.keepFiles:
try: os.remove(tmpSol)
except: pass
try: os.remove("cplex.log")
except: pass
if status != LpStatusInfeasible:
lp.assignVarsVals(values)
lp.assignVarsDj(reducedCosts)
lp.assignConsPi(shadowPrices)
lp.assignConsSlack(slacks)
lp.status = status
return status
def readsol(self,filename):
"""Read a CPLEX solution file"""
try:
import xml.etree.ElementTree as et
except ImportError:
import elementtree.ElementTree as et
solutionXML = et.parse(filename).getroot()
solutionheader = solutionXML.find("header")
statusString = solutionheader.get("solutionStatusString")
cplexStatus = {
"optimal":LpStatusOptimal,
}
if statusString not in cplexStatus:
raise PulpSolverError("Unknown status returned by CPLEX: "+statusString)
status = cplexStatus[statusString]
shadowPrices = {}
slacks = {}
shadowPrices = {}
slacks = {}
constraints = solutionXML.find("linearConstraints")
for constraint in constraints:
name = constraint.get("name")
shadowPrice = constraint.get("dual")
slack = constraint.get("slack")
shadowPrices[name] = float(shadowPrice)
slacks[name] = float(slack)
values = {}
reducedCosts = {}
for variable in solutionXML.find("variables"):
name = variable.get("name")
value = variable.get("value")
reducedCost = variable.get("reducedCost")
values[name] = float(value)
reducedCosts[name] = float(reducedCost)
return status, values, reducedCosts, shadowPrices, slacks
def CPLEX_DLL_load_dll(path):
"""
function that loads the DLL useful for debugging installation problems
"""
import ctypes
if os.name in ['nt','dos']:
lib = ctypes.windll.LoadLibrary(path)
else:
lib = ctypes.cdll.LoadLibrary(path)
return lib
try:
import ctypes
class CPLEX_DLL(LpSolver):
"""
The CPLEX LP/MIP solver (via a Dynamic library DLL - windows or SO - Linux)
This solver wraps the c library api of cplex.
It has been tested against cplex 11.
For api functions that have not been wrapped in this solver please use
the ctypes library interface to the cplex api in CPLEX_DLL.lib
"""
lib = CPLEX_DLL_load_dll(cplex_dll_path)
#parameters manually found in solver manual
CPX_PARAM_EPGAP = 2009
CPX_PARAM_MEMORYEMPHASIS = 1082 # from Cplex 11.0 manual
CPX_PARAM_TILIM = 1039
CPX_PARAM_LPMETHOD = 1062
#argtypes for CPLEX functions
lib.CPXsetintparam.argtypes = [ctypes.c_void_p,
ctypes.c_int, ctypes.c_int]
lib.CPXsetdblparam.argtypes = [ctypes.c_void_p, ctypes.c_int,
ctypes.c_double]
lib.CPXfopen.argtypes = [ctypes.c_char_p,
ctypes.c_char_p]
lib.CPXfopen.restype = ctypes.c_void_p
lib.CPXsetlogfile.argtypes = [ctypes.c_void_p,
ctypes.c_void_p]
def __init__(self,
mip = True,
msg = True,
timeLimit = None,
epgap = None,
logfilename = None,
emphasizeMemory = False):
"""
Initializes the CPLEX_DLL solver.
@param mip: if False the solver will solve a MIP as an LP
@param msg: displays information from the solver to stdout
@param epgap: sets the integer bound gap
@param logfilename: sets the filename of the cplex logfile
@param emphasizeMemory: makes the solver emphasize Memory over
solution time
"""
LpSolver.__init__(self, mip, msg)
self.timeLimit = timeLimit
self.grabLicence()
self.setMemoryEmphasis(emphasizeMemory)
if epgap is not None:
self.changeEpgap(epgap)
if timeLimit is not None:
self.setTimeLimit(timeLimit)
if logfilename is not None:
self.setlogfile(logfilename)
else:
self.logfile = None
def setlogfile(self, filename):
"""
sets the logfile for cplex output
"""
self.logfilep = CPLEX_DLL.lib.CPXfopen(filename, "w")
CPLEX_DLL.lib.CPXsetlogfile(self.env, self.logfilep)
def changeEpgap(self, epgap = 10**-4):
"""
Change cplex solver integer bound gap tolerence
"""
CPLEX_DLL.lib.CPXsetdblparam(self.env,CPLEX_DLL.CPX_PARAM_EPGAP,
epgap)
def setLpAlgorithm(self, algo):
"""
Select the LP algorithm to use.
See your CPLEX manual for valid values of algo. For CPLEX
12.1 these are 0 for "automatic", 1 primal, 2 dual, 3 network, 4
barrier, 5 sifting and 6 concurrent. Currently the default setting
0 always choooses dual simplex.
"""
CPLEX_DLL.lib.CPXsetintparam(self.env,CPLEX_DLL.CPX_PARAM_LPMETHOD,
algo)
def setTimeLimit(self, timeLimit = 0.0):
"""
Make cplex limit the time it takes --added CBM 8/28/09
"""
CPLEX_DLL.lib.CPXsetdblparam(self.env,CPLEX_DLL.CPX_PARAM_TILIM,
float(timeLimit))
def setMemoryEmphasis(self, yesOrNo = False):
"""
Make cplex try to conserve memory at the expense of
performance.
"""
CPLEX_DLL.lib.CPXsetintparam(self.env,
CPLEX_DLL.CPX_PARAM_MEMORYEMPHASIS,yesOrNo)
def findSolutionValues(self, lp, numcols, numrows):
byref = ctypes.byref
solutionStatus = ctypes.c_int()
objectiveValue = ctypes.c_double()
x = (ctypes.c_double * numcols)()
pi = (ctypes.c_double * numrows)()
slack = (ctypes.c_double * numrows)()
dj = (ctypes.c_double * numcols)()
status= CPLEX_DLL.lib.CPXsolwrite(self.env, self.hprob,
"CplexTest.sol")
if lp.isMIP():
solutionStatus.value = CPLEX_DLL.lib.CPXgetstat(self.env,
self.hprob)
status = CPLEX_DLL.lib.CPXgetobjval(self.env, self.hprob,
byref(objectiveValue))
if status != 0 and status != 1217: #no solution exists
raise PulpSolverError("Error in CPXgetobjval status="
+ str(status))
status = CPLEX_DLL.lib.CPXgetx(self.env, self.hprob,
byref(x), 0, numcols - 1)
if status != 0 and status != 1217:
raise PulpSolverError("Error in CPXgetx status=" + str(status))
else:
status = CPLEX_DLL.lib.CPXsolution(self.env, self.hprob,
byref(solutionStatus),
byref(objectiveValue),
byref(x), byref(pi),
byref(slack), byref(dj))
# 102 is the cplex return status for
# integer optimal within tolerance
# and is useful for breaking symmetry.
CplexLpStatus = {1: LpStatusOptimal, 3: LpStatusInfeasible,
2: LpStatusUnbounded, 0: LpStatusNotSolved,
101: LpStatusOptimal, 102: LpStatusOptimal,
103: LpStatusInfeasible}
#populate pulp solution values
variablevalues = {}
variabledjvalues = {}
constraintpivalues = {}
constraintslackvalues = {}
for i in range(numcols):
variablevalues[self.n2v[i].name] = x[i]
variabledjvalues[self.n2v[i].name] = dj[i]
lp.assignVarsVals(variablevalues)
lp.assignVarsDj(variabledjvalues)
#put pi and slack variables against the constraints
for i in range(numrows):
constraintpivalues[self.n2c[i]] = pi[i]
constraintslackvalues[self.n2c[i]] = slack[i]
lp.assignConsPi(constraintpivalues)
lp.assignConsSlack(constraintslackvalues)
#TODO: clear up the name of self.n2c
if self.msg:
print("Cplex status=", solutionStatus.value)
lp.resolveOK = True
for var in lp.variables():
var.isModified = False
lp.status = CplexLpStatus.get(solutionStatus.value,
LpStatusUndefined)
return lp.status
def __del__(self):
#LpSolver.__del__(self)
self.releaseLicence()
def available(self):
"""True if the solver is available"""
return True
def grabLicence(self):
"""
Returns True if a CPLEX licence can be obtained.
The licence is kept until releaseLicence() is called.
"""
status = ctypes.c_int()
# If the config file allows to do so (non null params), try to
# grab a runtime license.
if ilm_cplex_license and ilm_cplex_license_signature:
runtime_status = CPLEX_DLL.lib.CPXsetstaringsol(
ilm_cplex_license,
ilm_cplex_license_signature)
# if runtime_status is not zero, running with a runtime
# license will fail. However, no error is thrown (yet)
# because the second call might still succeed if the user
# has another license. Let us forgive bad user
# configuration:
if not (runtime_status == 0) and self.msg:
print(
"CPLEX library failed to load the runtime license" +
"the call returned status=%s" % str(runtime_status) +
"Please check the pulp config file.")
self.env = CPLEX_DLL.lib.CPXopenCPLEX(ctypes.byref(status))
self.hprob = None
if not(status.value == 0):
raise PulpSolverError("CPLEX library failed on " +
"CPXopenCPLEX status=" + str(status))
def releaseLicence(self):
"""Release a previously obtained CPLEX licence"""
if getattr(self,"env",False):
status=CPLEX_DLL.lib.CPXcloseCPLEX(self.env)
self.env = self.hprob = None
else:
raise PulpSolverError("No CPLEX enviroment to close")
def callSolver(self, isMIP):
"""Solves the problem with cplex
"""
#solve the problem
self.cplexTime = -clock()
if isMIP and self.mip:
status= CPLEX_DLL.lib.CPXmipopt(self.env, self.hprob)
if status != 0:
raise PulpSolverError("Error in CPXmipopt status="
+ str(status))
else:
status = CPLEX_DLL.lib.CPXlpopt(self.env, self.hprob)
if status != 0:
raise PulpSolverError("Error in CPXlpopt status="
+ str(status))
self.cplexTime += clock()
def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
#TODO alter so that msg parameter is handled correctly
status = ctypes.c_int()
byref = ctypes.byref #shortcut to function
if self.hprob is not None:
CPLEX_DLL.lib.CPXfreeprob(self.env, self.hprob)
self.hprob = CPLEX_DLL.lib.CPXcreateprob(self.env,
byref(status), lp.name)
if status.value != 0:
raise PulpSolverError("Error in CPXcreateprob status="
+ str(status))
(numcols, numrows, numels, rangeCount,
objSense, obj, objconst,
rhs, rangeValues, rowSense, matbeg, matcnt, matind,
matval, lb, ub, initValues, colname,
rowname, xctype, n2v, n2c )= self.getCplexStyleArrays(lp)
status.value = CPLEX_DLL.lib.CPXcopylpwnames (self.env, self.hprob,
numcols, numrows,
objSense, obj, rhs, rowSense, matbeg, matcnt,
matind, matval, lb, ub, None, colname, rowname)
if status.value != 0:
raise PulpSolverError("Error in CPXcopylpwnames status=" +
str(status))
if lp.isMIP() and self.mip:
status.value = CPLEX_DLL.lib.CPXcopyctype(self.env,
self.hprob,
xctype)
if status.value != 0:
raise PulpSolverError("Error in CPXcopyctype status=" +
str(status))
#set the initial solution
self.callSolver(lp.isMIP())
#get the solution information
solutionStatus = self.findSolutionValues(lp, numcols, numrows)
for var in lp.variables():
var.modified = False
return solutionStatus
def actualResolve(self,lp):
"""looks at which variables have been modified and changes them
"""
#TODO: Add changing variables not just adding them
#TODO: look at constraints
modifiedVars = [var for var in lp.variables() if var.modified]
#assumes that all variables flagged as modified
#need to be added to the problem
newVars = modifiedVars
#print newVars
self.v2n.update([(var, i+self.addedVars)
for i,var in enumerate(newVars)])
self.n2v.update([(i+self.addedVars, var)
for i,var in enumerate(newVars)])
self.vname2n.update([(var.name, i+self.addedVars)
for i,var in enumerate(newVars)])
oldVars = self.addedVars
self.addedVars += len(newVars)
(ccnt,nzcnt,obj,cmatbeg,
cmatlen, cmatind,cmatval,
lb,ub, initvals,
colname, coltype) = self.getSparseCols(newVars, lp, oldVars,
defBound = 1e20)
CPXaddcolsStatus = CPLEX_DLL.lib.CPXaddcols(self.env, self.hprob,
ccnt, nzcnt,
obj,cmatbeg,
cmatind,cmatval,
lb,ub,colname)
#add the column types
if lp.isMIP() and self.mip:
indices = (ctypes.c_int * len(newVars))()
for i,var in enumerate(newVars):
indices[i] = oldVars +i
CPXchgctypeStatus = CPLEX_DLL.lib.CPXchgctype (self.env,
self.hprob,
ccnt, indices, coltype);
#solve the problem
self.callSolver(lp.isMIP())
#get the solution information
solutionStatus = self.findSolutionValues(lp, self.addedVars,
self.addedRows)
for var in modifiedVars:
var.modified = False
return solutionStatus
def getSparseCols(self, vars, lp, offset = 0, defBound = 1e20):
"""
outputs the variables in var as a sparse matrix,
suitable for cplex and Coin
Copyright (c) Stuart Mitchell 2007
"""
numVars = len(vars)
obj = (ctypes.c_double * numVars)()
cmatbeg = (ctypes.c_int * numVars)()
mycmatind = []
mycmatval = []
rangeCount = 0
#values for variables
colNames = (ctypes.c_char_p * numVars)()
lowerBounds = (ctypes.c_double * numVars)()
upperBounds = (ctypes.c_double * numVars)()
initValues = (ctypes.c_double * numVars)()
i=0
for v in vars:
colNames[i] = str(v.name)
initValues[i] = v.init
if v.lowBound != None:
lowerBounds[i] = v.lowBound
else:
lowerBounds[i] = -defBound
if v.upBound != None:
upperBounds[i] = v.upBound
else:
upperBounds[i] = defBound
i+= 1
#create the new variables
#values for constraints
#return the coefficient matrix as a series of vectors
myobjectCoeffs = {}
numRows = len(lp.constraints)
sparseMatrix = sparse.Matrix(list(range(numRows)), list(range(numVars)))
for var in vars:
for row,coeff in var.expression.items():
if row.name == lp.objective.name:
myobjectCoeffs[var] = coeff
else:
sparseMatrix.add(self.c2n[row.name], self.v2n[var] - offset, coeff)
#objective values
objectCoeffs = (ctypes.c_double * numVars)()
for var in vars:
objectCoeffs[self.v2n[var]-offset] = myobjectCoeffs[var]
(numels, mystartsBase, mylenBase, myindBase,
myelemBase) = sparseMatrix.col_based_arrays()
elemBase = ctypesArrayFill(myelemBase, ctypes.c_double)
indBase = ctypesArrayFill(myindBase, ctypes.c_int)
startsBase = ctypesArrayFill(mystartsBase, ctypes.c_int)
lenBase = ctypesArrayFill(mylenBase, ctypes.c_int)
#MIP Variables
NumVarCharArray = ctypes.c_char * numVars
columnType = NumVarCharArray()
if lp.isMIP():
CplexLpCategories = {LpContinuous: "C",
LpInteger: "I"}
for v in vars:
columnType[self.v2n[v] - offset] = CplexLpCategories[v.cat]
return numVars, numels, objectCoeffs, \
startsBase, lenBase, indBase, \
elemBase, lowerBounds, upperBounds, initValues, colNames, \
columnType
def objSa(self, vars = None):
"""Objective coefficient sensitivity analysis.
Called after a problem has been solved, this function
returns a dict mapping variables to pairs (lo, hi) indicating
that the objective coefficient of the variable can vary
between lo and hi without changing the optimal basis
(if other coefficients remain constant). If an iterable
vars is given, results are returned only for variables in vars.
"""
if vars is None:
v2n = self.v2n
else:
v2n = dict((v, self.v2n[v]) for v in vars)
ifirst = min(v2n.values())
ilast = max(v2n.values())
row_t = ctypes.c_double * (ilast - ifirst + 1)
lo = row_t()
hi = row_t()
status = ctypes.c_int()
status.value = CPLEX_DLL.lib.CPXobjsa(self.env, self.hprob,
ifirst, ilast, lo, hi)
if status.value != 0:
raise PulpSolverError("Error in CPXobjsa, status="
+ str(status))
return dict((v, (lo[i - ifirst], hi[i - ifirst]))
for v, i in v2n.items())
CPLEX = CPLEX_DLL
except (ImportError,OSError):
class CPLEX_DLL(LpSolver):
"""The CPLEX LP/MIP solver PHANTOM Something went wrong!!!!"""
def available(self):
"""True if the solver is available"""
return False
def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
raise PulpSolverError("CPLEX_DLL: Not Available")
CPLEX = CPLEX_CMD
try:
import cplex
except (ImportError):
class CPLEX_PY(LpSolver):
"""The CPLEX LP/MIP solver from python PHANTOM Something went wrong!!!!"""
def available(self):
"""True if the solver is available"""
return False
def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
raise PulpSolverError("CPLEX_PY: Not Available")
else:
class CPLEX_PY(LpSolver):
"""
The CPLEX LP/MIP solver (via a Python Binding)
This solver wraps the python api of cplex.
It has been tested against cplex 12.3.
For api functions that have not been wrapped in this solver please use
the base cplex classes
"""
CplexLpStatus = {cplex.Cplex.solution.status.MIP_optimal: LpStatusOptimal,
cplex.Cplex.solution.status.optimal: LpStatusOptimal,
cplex.Cplex.solution.status.optimal_tolerance: LpStatusOptimal,
cplex.Cplex.solution.status.infeasible: LpStatusInfeasible,
cplex.Cplex.solution.status.infeasible_or_unbounded: LpStatusInfeasible,
cplex.Cplex.solution.status.MIP_infeasible: LpStatusInfeasible,
cplex.Cplex.solution.status.MIP_infeasible_or_unbounded: LpStatusInfeasible,
cplex.Cplex.solution.status.unbounded: LpStatusUnbounded,
cplex.Cplex.solution.status.MIP_unbounded: LpStatusUnbounded,
cplex.Cplex.solution.status.abort_dual_obj_limit: LpStatusNotSolved,
cplex.Cplex.solution.status.abort_iteration_limit: LpStatusNotSolved,
cplex.Cplex.solution.status.abort_obj_limit: LpStatusNotSolved,
cplex.Cplex.solution.status.abort_relaxed: LpStatusNotSolved,
cplex.Cplex.solution.status.abort_time_limit: LpStatusNotSolved,
cplex.Cplex.solution.status.abort_user: LpStatusNotSolved,
}
def __init__(self,
mip = True,
msg = True,
timeLimit = None,
epgap = None,
logfilename = None):
"""
Initializes the CPLEX_PY solver.
@param mip: if False the solver will solve a MIP as an LP
@param msg: displays information from the solver to stdout
@param epgap: sets the integer bound gap
@param logfilename: sets the filename of the cplex logfile
"""
LpSolver.__init__(self, mip, msg)
self.timeLimit = timeLimit
self.epgap = epgap
self.logfilename = logfilename
def available(self):
"""True if the solver is available"""
return True
def actualSolve(self, lp, callback = None):
"""
Solve a well formulated lp problem
creates a cplex model, variables and constraints and attaches
them to the lp model which it then solves
"""
self.buildSolverModel(lp)
#set the initial solution
log.debug("Solve the Model using cplex")
self.callSolver(lp)
#get the solution information
solutionStatus = self.findSolutionValues(lp)
for var in lp.variables():
var.modified = False
for constraint in lp.constraints.values():
constraint.modified = False
return solutionStatus
def buildSolverModel(self, lp):
"""
Takes the pulp lp model and translates it into a cplex model
"""
self.n2v = dict((var.name, var) for var in lp.variables())
if len(self.n2v) != len(lp.variables()):
raise PulpSolverError(
'Variables must have unique names for cplex solver')
log.debug("create the cplex model")
self.solverModel = lp.solverModel = cplex.Cplex()
log.debug("set the name of the problem")
if not self.mip:
self.solverModel.set_problem_name(lp.name)
log.debug("set the sense of the problem")
if lp.sense == LpMaximize:
lp.solverModel.objective.set_sense(
lp.solverModel.objective.sense.maximize)
obj = [float(lp.objective.get(var, 0.0)) for var in lp.variables()]
def cplex_var_lb(var):
if var.lowBound is not None:
return float(var.lowBound)
else:
return -cplex.infinity
lb = [cplex_var_lb(var) for var in lp.variables()]
def cplex_var_ub(var):
if var.upBound is not None:
return float(var.upBound)
else:
return cplex.infinity
ub = [cplex_var_ub(var) for var in lp.variables()]
colnames = [var.name for var in lp.variables()]
def cplex_var_types(var):
if var.cat == LpInteger:
return 'I'
else:
return 'C'
ctype = [cplex_var_types(var) for var in lp.variables()]
ctype = "".join(ctype)
lp.solverModel.variables.add(obj=obj, lb=lb, ub=ub, types=ctype,
names=colnames)
rows = []
senses = []
rhs = []
rownames = []
for name,constraint in lp.constraints.items():
#build the expression
expr = [(var.name, float(coeff)) for var, coeff in constraint.items()]
if not expr:
#if the constraint is empty
rows.append(([],[]))
else:
rows.append(list(zip(*expr)))
if constraint.sense == LpConstraintLE:
senses.append('L')
elif constraint.sense == LpConstraintGE:
senses.append('G')
elif constraint.sense == LpConstraintEQ:
senses.append('E')
else:
raise PulpSolverError('Detected an invalid constraint type')
rownames.append(name)
rhs.append(float(-constraint.constant))
lp.solverModel.linear_constraints.add(lin_expr=rows, senses=senses,
rhs=rhs, names=rownames)
log.debug("set the type of the problem")
if not self.mip:
self.solverModel.set_problem_type(cplex.Cplex.problem_type.LP)
log.debug("set the logging")
if not self.msg:
self.solverModel.set_error_stream(None)
self.solverModel.set_log_stream(None)
self.solverModel.set_warning_stream(None)
self.solverModel.set_results_stream(None)
if self.logfilename is not None:
self.setlogfile(self.logfilename)
if self.epgap is not None:
self.changeEpgap(self.epgap)
if self.timeLimit is not None:
self.setTimeLimit(self.timeLimit)
def setlogfile(self, filename):
"""
sets the logfile for cplex output
"""
self.solverModel.set_log_stream(filename)
def changeEpgap(self, epgap = 10**-4):
"""
Change cplex solver integer bound gap tolerence
"""
self.solverModel.parameters.mip.tolerances.mipgap.set(epgap)
def setTimeLimit(self, timeLimit = 0.0):
"""
Make cplex limit the time it takes --added CBM 8/28/09
"""
self.solverModel.parameters.timelimit.set(timeLimit)
def callSolver(self, isMIP):
"""Solves the problem with cplex
"""
#solve the problem
self.solveTime = -clock()
self.solverModel.solve()
self.solveTime += clock()
def findSolutionValues(self, lp):
lp.cplex_status = lp.solverModel.solution.get_status()
lp.status = self.CplexLpStatus.get(lp.cplex_status, LpStatusUndefined)
var_names = [var.name for var in lp.variables()]
con_names = [con for con in lp.constraints]
try:
objectiveValue = lp.solverModel.solution.get_objective_value()
variablevalues = dict(zip(var_names, lp.solverModel.solution.get_values(var_names)))
lp.assignVarsVals(variablevalues)
constraintslackvalues = dict(zip(con_names, lp.solverModel.solution.get_linear_slacks(con_names)))
lp.assignConsSlack(constraintslackvalues)
if lp.solverModel.get_problem_type == cplex.Cplex.problem_type.LP:
variabledjvalues = dict(zip(var_names, lp.solverModel.solution.get_reduced_costs(var_names)))
lp.assignVarsDj(variabledjvalues)
constraintpivalues = dict(zip(con_names, lp.solverModel.solution.get_dual_values(con_names)))
lp.assignConsPi(constraintpivalues)
except cplex.exceptions.CplexSolverError:
#raises this error when there is no solution
pass
#put pi and slack variables against the constraints
#TODO: clear up the name of self.n2c
if self.msg:
print("Cplex status=", lp.cplex_status)
lp.resolveOK = True
for var in lp.variables():
var.isModified = False
return lp.status
def actualResolve(self,lp):
"""
looks at which variables have been modified and changes them
"""
raise NotImplementedError("Resolves in CPLEX_PY not yet implemented")
CPLEX = CPLEX_PY
class XPRESS(LpSolver_CMD):
"""The XPRESS LP solver"""
def __init__(self, path = None, keepFiles = 0, mip = 1,
msg = 0, maxSeconds = None, targetGap = None, heurFreq = None,
heurStra = None, coverCuts = None, preSolve = None,
options = []):
"""
Initializes the Xpress solver.
@param maxSeconds: the maximum time that the Optimizer will run before it terminates
@param targetGap: global search will terminate if:
abs(MIPOBJVAL - BESTBOUND) <= MIPRELSTOP * BESTBOUND
@param heurFreq: the frequency at which heuristics are used in the tree search
@param heurStra: heuristic strategy
@param coverCuts: the number of rounds of lifted cover inequalities at the top node
@param preSolve: whether presolving should be performed before the main algorithm
@param options: Adding more options, e.g. options = ["NODESELECTION=1", "HEURDEPTH=5"]
More about Xpress options and control parameters please see
http://tomopt.com/docs/xpress/tomlab_xpress008.php
"""
LpSolver_CMD.__init__(self, path, keepFiles, mip, msg, options)
self.maxSeconds = maxSeconds
self.targetGap = targetGap
self.heurFreq = heurFreq
self.heurStra = heurStra
self.coverCuts = coverCuts
self.preSolve = preSolve
def defaultPath(self):
return self.executableExtension("optimizer")
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)
if not self.keepFiles:
pid = os.getpid()
tmpLp = os.path.join(self.tmpDir, "%d-pulp.lp" % pid)
tmpSol = os.path.join(self.tmpDir, "%d-pulp.prt" % pid)
else:
tmpLp = lp.name+"-pulp.lp"
tmpSol = lp.name+"-pulp.prt"
lp.writeLP(tmpLp, writeSOS = 1, mip = self.mip)
if not self.msg:
xpress = os.popen(self.path+" "+lp.name+" > /dev/null 2> /dev/null", "w")
else:
xpress = os.popen(self.path+" "+lp.name, "w")
xpress.write("READPROB "+tmpLp+"\n")
if self.maxSeconds:
xpress.write("MAXTIME=%d\n" % self.maxSeconds)
if self.targetGap:
xpress.write("MIPRELSTOP=%f\n" % self.targetGap)
if self.heurFreq:
xpress.write("HEURFREQ=%d\n" % self.heurFreq)
if self.heurStra:
xpress.write("HEURSTRATEGY=%d\n" % self.heurStra)
if self.coverCuts:
xpress.write("COVERCUTS=%d\n" % self.coverCuts)
if self.preSolve:
xpress.write("PRESOLVE=%d\n" % self.preSolve)
for option in self.options:
xpress.write(option+"\n")
if lp.sense == LpMaximize:
xpress.write("MAXIM\n")
else:
xpress.write("MINIM\n")
if lp.isMIP() and self.mip:
xpress.write("GLOBAL\n")
xpress.write("WRITEPRTSOL "+tmpSol+"\n")
xpress.write("QUIT\n")
if xpress.close() != None:
raise PulpSolverError("PuLP: Error while executing "+self.path)
status, values = self.readsol(tmpSol)
if not self.keepFiles:
try: os.remove(tmpLp)
except: pass
try: os.remove(tmpSol)
except: pass
lp.status = status
lp.assignVarsVals(values)
if abs(lp.infeasibilityGap(self.mip)) > 1e-5: # Arbitrary
lp.status = LpStatusInfeasible
return lp.status
def readsol(self,filename):
"""Read an XPRESS solution file"""
with open(filename) as f:
for i in range(6): f.readline()
l = f.readline().split()
rows = int(l[2])
cols = int(l[5])
for i in range(3): f.readline()
statusString = f.readline().split()[0]
xpressStatus = {
"Optimal":LpStatusOptimal,
}
if statusString not in xpressStatus:
raise PulpSolverError("Unknown status returned by XPRESS: "+statusString)
status = xpressStatus[statusString]
values = {}
while 1:
l = f.readline()
if l == "": break
line = l.split()
if len(line) and line[0] == 'C':
name = line[2]
value = float(line[4])
values[name] = value
return status, values
class COIN_CMD(LpSolver_CMD):
"""The COIN CLP/CBC LP solver
now only uses cbc
"""
def defaultPath(self):
return self.executableExtension(cbc_path)
def __init__(self, path = None, keepFiles = 0, mip = 1,
msg = 0, cuts = None, presolve = None, dual = None,
strong = None, options = [],
fracGap = None, maxSeconds = None, threads = None):
LpSolver_CMD.__init__(self, path, keepFiles, mip, msg, options)
self.cuts = cuts
self.presolve = presolve
self.dual = dual
self.strong = strong
self.fracGap = fracGap
self.maxSeconds = maxSeconds
self.threads = threads
#TODO hope this gets fixed in cbc as it does not like the c:\ in windows paths
if os.name == 'nt':
self.tmpDir = ''
def copy(self):
"""Make a copy of self"""
aCopy = LpSolver_CMD.copy(self)
aCopy.cuts = self.cuts
aCopy.presolve = self.presolve
aCopy.dual = self.dual
aCopy.strong = self.strong
return aCopy
def actualSolve(self, lp, **kwargs):
"""Solve a well formulated lp problem"""
return self.solve_CBC(lp, **kwargs)
def available(self):
"""True if the solver is available"""
return self.executable(self.path)
def solve_CBC(self, lp, use_mps=True):
"""Solve a MIP problem using CBC"""
if not self.executable(self.path):
raise PulpSolverError("Pulp: cannot execute %s cwd: %s"%(self.path,
os.getcwd()))
if not self.keepFiles:
pid = os.getpid()
tmpLp = os.path.join(self.tmpDir, "%d-pulp.lp" % pid)
tmpMps = os.path.join(self.tmpDir, "%d-pulp.mps" % pid)
tmpSol = os.path.join(self.tmpDir, "%d-pulp.sol" % pid)
else:
tmpLp = lp.name+"-pulp.lp"
tmpMps = lp.name+"-pulp.mps"
tmpSol = lp.name+"-pulp.sol"
if use_mps:
vs, variablesNames, constraintsNames, objectiveName = lp.writeMPS(
tmpMps, rename = 1)
cmds = ' '+tmpMps+" "
if lp.sense == LpMaximize:
cmds += 'max '
else:
lp.writeLP(tmpLp)
cmds = ' '+tmpLp+" "
if self.threads:
cmds += "threads %s "%self.threads
if self.fracGap is not None:
cmds += "ratio %s "%self.fracGap
if self.maxSeconds is not None:
cmds += "sec %s "%self.maxSeconds
if self.presolve:
cmds += "presolve on "
if self.strong:
cmds += "strong %d " % self.strong
if self.cuts:
cmds += "gomory on "
#cbc.write("oddhole on "
cmds += "knapsack on "
cmds += "probing on "
for option in self.options:
cmds += option+" "
if self.mip:
cmds += "branch "
else:
cmds += "initialSolve "
cmds += "printingOptions all "
cmds += "solution "+tmpSol+" "
if self.msg:
pipe = None
else:
pipe = open(os.devnull, 'w')
log.debug(self.path + cmds)
cbc = subprocess.Popen((self.path + cmds).split(), stdout = pipe,
stderr = pipe)
if cbc.wait() != 0:
raise PulpSolverError("Pulp: Error while trying to execute " + \
self.path)
if not os.path.exists(tmpSol):
raise PulpSolverError("Pulp: Error while executing "+self.path)
if use_mps:
lp.status, values, reducedCosts, shadowPrices, slacks = self.readsol_MPS(
tmpSol, lp, lp.variables(),
variablesNames, constraintsNames, objectiveName)
else:
lp.status, values, reducedCosts, shadowPrices, slacks = self.readsol_LP(
tmpSol, lp, lp.variables())
lp.assignVarsVals(values)
lp.assignVarsDj(reducedCosts)
lp.assignConsPi(shadowPrices)
lp.assignConsSlack(slacks, activity=True)
if not self.keepFiles:
try:
os.remove(tmpMps)
except:
pass
try:
os.remove(tmpLp)
except:
pass
try:
os.remove(tmpSol)
except:
pass
return lp.status
def readsol_MPS(self, filename, lp, vs, variablesNames, constraintsNames,
objectiveName):
"""
Read a CBC solution file generated from an mps file (different names)
"""
values = {}
reverseVn = {}
for k, n in variablesNames.items():
reverseVn[n] = k
reverseCn = {}
for k, n in constraintsNames.items():
reverseCn[n] = k
for v in vs:
values[v.name] = 0.0
reducedCosts = {}
shadowPrices = {}
slacks = {}
cbcStatus = {'Optimal': LpStatusOptimal,
'Infeasible': LpStatusInfeasible,
'Unbounded': LpStatusUnbounded,
'Stopped': LpStatusNotSolved}
with open(filename) as f:
statusstr = f.readline().split()[0]
status = cbcStatus.get(statusstr, LpStatusUndefined)
for l in f:
if len(l)<=2:
break
l = l.split()
#incase the solution is infeasible
if l[0] == '**':
l = l[1:]
vn = l[1]
val = l[2]
dj = l[3]
if vn in reverseVn:
values[reverseVn[vn]] = float(val)
reducedCosts[reverseVn[vn]] = float(dj)
if vn in reverseCn:
slacks[reverseCn[vn]] = float(val)
shadowPrices[reverseCn[vn]] = float(dj)
return status, values, reducedCosts, shadowPrices, slacks
def readsol_LP(self, filename, lp, vs):
"""
Read a CBC solution file generated from an lp (good names)
"""
values = {}
reducedCosts = {}
shadowPrices = {}
slacks = {}
for v in vs:
values[v.name] = 0.0
cbcStatus = {'Optimal': LpStatusOptimal,
'Infeasible': LpStatusInfeasible,
'Unbounded': LpStatusUnbounded,
'Stopped': LpStatusNotSolved}
with open(filename) as f:
statusstr = f.readline().split()[0]
status = cbcStatus.get(statusstr, LpStatusUndefined)
for l in f:
if len(l)<=2:
break
l = l.split()
if l[0] == '**':
l = l[1:]
vn = l[1]
val = l[2]
dj = l[3]
if vn in values:
values[vn] = float(val)
reducedCosts[vn] = float(dj)
if vn in lp.constraints:
slacks[vn] = float(val)
shadowPrices[vn] = float(dj)
return status, values, reducedCosts, shadowPrices, slacks
COIN = COIN_CMD
class PULP_CBC_CMD(COIN_CMD):
"""
This solver uses a precompiled version of cbc provided with the package
"""
pulp_cbc_path = pulp_cbc_path
try:
if os.name != 'nt':
if not os.access(pulp_cbc_path, os.X_OK):
import stat
os.chmod(pulp_cbc_path, stat.S_IXUSR + stat.S_IXOTH)
except: #probably due to incorrect permissions
def available(self):
"""True if the solver is available"""
return False
def actualSolve(self, lp, callback = None):
"""Solve a well formulated lp problem"""
raise PulpSolverError("PULP_CBC_CMD: Not Available (check permissions on %s)" % self.pulp_cbc_path)
else:
def __init__(self, path=None, *args, **kwargs):
"""
just loads up COIN_CMD with the path set
"""
if path is not None:
raise PulpSolverError('Use COIN_CMD if you want to set a path')
#check that the file is executable
COIN_CMD.__init__(self, path=self.pulp_cbc_path, *args, **kwargs)
def COINMP_DLL_load_dll(path):
"""
function that loads the DLL useful for debugging installation problems
"""
import ctypes
if os.name == 'nt':
lib = ctypes.windll.LoadLibrary(path[-1])
else:
#linux hack to get working
mode = ctypes.RTLD_GLOBAL
for libpath in path[:-1]:
#RTLD_LAZY = 0x00001
ctypes.CDLL(libpath, mode = mode)
lib = ctypes.CDLL(path[-1], mode = mode)
return lib
class COINMP_DLL(LpSolver):
"""
The COIN_MP LP MIP solver (via a DLL or linux so)
:param timeLimit: The number of seconds before forcing the solver to exit
:param epgap: The fractional mip tolerance
"""
try:
lib = COINMP_DLL_load_dll(coinMP_path)
except (ImportError, OSError):
@classmethod
def available(cls):
"""True if the solver is available"""
return False
def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
raise PulpSolverError("COINMP_DLL: Not Available")
else:
COIN_INT_LOGLEVEL = 7
COIN_REAL_MAXSECONDS = 16
COIN_REAL_MIPMAXSEC = 19
COIN_REAL_MIPFRACGAP = 34
lib.CoinGetInfinity.restype = ctypes.c_double
lib.CoinGetVersionStr.restype = ctypes.c_char_p
lib.CoinGetSolutionText.restype=ctypes.c_char_p
lib.CoinGetObjectValue.restype=ctypes.c_double
lib.CoinGetMipBestBound.restype=ctypes.c_double
def __init__(self, mip = 1, msg = 1, cuts = 1, presolve = 1, dual = 1,
crash = 0, scale = 1, rounding = 1, integerPresolve = 1, strong = 5,
timeLimit = None, epgap = None):
LpSolver.__init__(self, mip, msg)
self.maxSeconds = None
if timeLimit is not None:
self.maxSeconds = float(timeLimit)
self.fracGap = None
if epgap is not None:
self.fracGap = float(epgap)
#Todo: these options are not yet implemented
self.cuts = cuts
self.presolve = presolve
self.dual = dual
self.crash = crash
self.scale = scale
self.rounding = rounding
self.integerPresolve = integerPresolve
self.strong = strong
def copy(self):
"""Make a copy of self"""
aCopy = LpSolver.copy()
aCopy.cuts = self.cuts
aCopy.presolve = self.presolve
aCopy.dual = self.dual
aCopy.crash = self.crash
aCopy.scale = self.scale
aCopy.rounding = self.rounding
aCopy.integerPresolve = self.integerPresolve
aCopy.strong = self.strong
return aCopy
@classmethod
def available(cls):
"""True if the solver is available"""
return True
def getSolverVersion(self):
"""
returns a solver version string
example:
>>> COINMP_DLL().getSolverVersion() # doctest: +ELLIPSIS
'...'
"""
return self.lib.CoinGetVersionStr()
def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
#TODO alter so that msg parameter is handled correctly
self.debug = 0
#initialise solver
self.lib.CoinInitSolver("")
#create problem
self.hProb = hProb = self.lib.CoinCreateProblem(lp.name);
#set problem options
if self.maxSeconds:
if self.mip:
self.lib.CoinSetRealOption(hProb, self.COIN_REAL_MIPMAXSEC,
ctypes.c_double(self.maxSeconds))
else:
self.lib.CoinSetRealOption(hProb, self.COIN_REAL_MAXSECONDS,
ctypes.c_double(self.maxSeconds))
if self.fracGap:
#Hopefully this is the bound gap tolerance
self.lib.CoinSetRealOption(hProb, self.COIN_REAL_MIPFRACGAP,
ctypes.c_double(self.fracGap))
#CoinGetInfinity is needed for varibles with no bounds
coinDblMax = self.lib.CoinGetInfinity()
if self.debug: print("Before getCoinMPArrays")
(numVars, numRows, numels, rangeCount,
objectSense, objectCoeffs, objectConst,
rhsValues, rangeValues, rowType, startsBase,
lenBase, indBase,
elemBase, lowerBounds, upperBounds, initValues, colNames,
rowNames, columnType, n2v, n2c) = self.getCplexStyleArrays(lp)
self.lib.CoinLoadProblem(hProb,
numVars, numRows, numels, rangeCount,
objectSense, objectConst, objectCoeffs,
lowerBounds, upperBounds, rowType,
rhsValues, rangeValues, startsBase,
lenBase, indBase, elemBase,
colNames, rowNames, "Objective")
if lp.isMIP() and self.mip:
self.lib.CoinLoadInteger(hProb,columnType)
if self.msg == 0:
#close stdout to get rid of messages
tempfile = open(mktemp(),'w')
savestdout = os.dup(1)
os.close(1)
if os.dup(tempfile.fileno()) != 1:
raise PulpSolverError("couldn't redirect stdout - dup() error")
self.coinTime = -clock()
self.lib.CoinOptimizeProblem(hProb, 0);
self.coinTime += clock()
if self.msg == 0:
#reopen stdout
os.close(1)
os.dup(savestdout)
os.close(savestdout)
CoinLpStatus = {0:LpStatusOptimal,
1:LpStatusInfeasible,
2:LpStatusInfeasible,
3:LpStatusNotSolved,
4:LpStatusNotSolved,
5:LpStatusNotSolved,
-1:LpStatusUndefined
}
solutionStatus = self.lib.CoinGetSolutionStatus(hProb)
solutionText = self.lib.CoinGetSolutionText(hProb,solutionStatus)
objectValue = self.lib.CoinGetObjectValue(hProb)
#get the solution values
NumVarDoubleArray = ctypes.c_double * numVars
NumRowsDoubleArray = ctypes.c_double * numRows
cActivity = NumVarDoubleArray()
cReducedCost = NumVarDoubleArray()
cSlackValues = NumRowsDoubleArray()
cShadowPrices = NumRowsDoubleArray()
self.lib.CoinGetSolutionValues(hProb, ctypes.byref(cActivity),
ctypes.byref(cReducedCost),
ctypes.byref(cSlackValues),
ctypes.byref(cShadowPrices))
variablevalues = {}
variabledjvalues = {}
constraintpivalues = {}
constraintslackvalues = {}
if lp.isMIP() and self.mip:
lp.bestBound = self.lib.CoinGetMipBestBound(hProb)
for i in range(numVars):
variablevalues[self.n2v[i].name] = cActivity[i]
variabledjvalues[self.n2v[i].name] = cReducedCost[i]
lp.assignVarsVals(variablevalues)
lp.assignVarsDj(variabledjvalues)
#put pi and slack variables against the constraints
for i in range(numRows):
constraintpivalues[self.n2c[i]] = cShadowPrices[i]
constraintslackvalues[self.n2c[i]] = \
rhsValues[i] - cSlackValues[i]
lp.assignConsPi(constraintpivalues)
lp.assignConsSlack(constraintslackvalues)
self.lib.CoinFreeSolver()
lp.status = CoinLpStatus[self.lib.CoinGetSolutionStatus(hProb)]
return lp.status
if COINMP_DLL.available():
COIN = COINMP_DLL
# to import the gurobipy name into the module scope
gurobipy = None
class GUROBI(LpSolver):
"""
The Gurobi LP/MIP solver (via its python interface)
The Gurobi variables are available (after a solve) in var.solverVar
Constriaints in constraint.solverConstraint
and the Model is in prob.solverModel
"""
try:
sys.path.append(gurobi_path)
# to import the name into the module scope
global gurobipy
import gurobipy
except: #FIXME: Bug because gurobi returns
#a gurobi exception on failed imports
def available(self):
"""True if the solver is available"""
return False
def actualSolve(self, lp, callback = None):
"""Solve a well formulated lp problem"""
raise PulpSolverError("GUROBI: Not Available")
else:
def __init__(self,
mip = True,
msg = True,
timeLimit = None,
epgap = None,
**solverParams):
"""
Initializes the Gurobi solver.
@param mip: if False the solver will solve a MIP as an LP
@param msg: displays information from the solver to stdout
@param timeLimit: sets the maximum time for solution
@param epgap: sets the integer bound gap
"""
LpSolver.__init__(self, mip, msg)
self.timeLimit = timeLimit
self.epgap = epgap
#set the output of gurobi
if not self.msg:
gurobipy.setParam("OutputFlag", 0)
#set the gurobi parameter values
for key,value in solverParams.items():
gurobipy.setParam(key, value)
def findSolutionValues(self, lp):
model = lp.solverModel
solutionStatus = model.Status
GRB = gurobipy.GRB
gurobiLpStatus = {GRB.OPTIMAL: LpStatusOptimal,
GRB.INFEASIBLE: LpStatusInfeasible,
GRB.INF_OR_UNBD: LpStatusInfeasible,
GRB.UNBOUNDED: LpStatusUnbounded,
GRB.ITERATION_LIMIT: LpStatusNotSolved,
GRB.NODE_LIMIT: LpStatusNotSolved,
GRB.TIME_LIMIT: LpStatusNotSolved,
GRB.SOLUTION_LIMIT: LpStatusNotSolved,
GRB.INTERRUPTED: LpStatusNotSolved,
GRB.NUMERIC: LpStatusNotSolved,
}
#populate pulp solution values
for var in lp.variables():
try:
var.varValue = var.solverVar.X
except gurobipy.GurobiError:
pass
try:
var.dj = var.solverVar.RC
except gurobipy.GurobiError:
pass
#put pi and slack variables against the constraints
for constr in lp.constraints.values():
try:
constr.pi = constr.solverConstraint.Pi
except gurobipy.GurobiError:
pass
try:
constr.slack = constr.solverConstraint.Slack
except gurobipy.GurobiError:
pass
if self.msg:
print("Gurobi status=", solutionStatus)
lp.resolveOK = True
for var in lp.variables():
var.isModified = False
lp.status = gurobiLpStatus.get(solutionStatus, LpStatusUndefined)
return lp.status
def available(self):
"""True if the solver is available"""
return True
def callSolver(self, lp, callback = None):
"""Solves the problem with gurobi
"""
#solve the problem
self.solveTime = -clock()
lp.solverModel.optimize(callback = callback)
self.solveTime += clock()
def buildSolverModel(self, lp):
"""
Takes the pulp lp model and translates it into a gurobi model
"""
log.debug("create the gurobi model")
lp.solverModel = gurobipy.Model(lp.name)
log.debug("set the sense of the problem")
if lp.sense == LpMaximize:
lp.solverModel.setAttr("ModelSense", -1)
if self.timeLimit:
lp.solverModel.setParam("TimeLimit", self.timeLimit)
if self.epgap:
lp.solverModel.setParam("MIPGap", self.epgap)
log.debug("add the variables to the problem")
for var in lp.variables():
lowBound = var.lowBound
if lowBound is None:
lowBound = -gurobipy.GRB.INFINITY
upBound = var.upBound
if upBound is None:
upBound = gurobipy.GRB.INFINITY
obj = lp.objective.get(var, 0.0)
varType = gurobipy.GRB.CONTINUOUS
if var.cat == LpInteger and self.mip:
varType = gurobipy.GRB.INTEGER
var.solverVar = lp.solverModel.addVar(lowBound, upBound,
vtype = varType,
obj = obj, name = var.name)
lp.solverModel.update()
log.debug("add the Constraints to the problem")
for name,constraint in lp.constraints.items():
#build the expression
expr = gurobipy.LinExpr(list(constraint.values()),
[v.solverVar for v in constraint.keys()])
if constraint.sense == LpConstraintLE:
relation = gurobipy.GRB.LESS_EQUAL
elif constraint.sense == LpConstraintGE:
relation = gurobipy.GRB.GREATER_EQUAL
elif constraint.sense == LpConstraintEQ:
relation = gurobipy.GRB.EQUAL
else:
raise PulpSolverError('Detected an invalid constraint type')
constraint.solverConstraint = lp.solverModel.addConstr(expr,
relation, -constraint.constant, name)
lp.solverModel.update()
def actualSolve(self, lp, callback = None):
"""
Solve a well formulated lp problem
creates a gurobi model, variables and constraints and attaches
them to the lp model which it then solves
"""
self.buildSolverModel(lp)
#set the initial solution
log.debug("Solve the Model using gurobi")
self.callSolver(lp, callback = callback)
#get the solution information
solutionStatus = self.findSolutionValues(lp)
for var in lp.variables():
var.modified = False
for constraint in lp.constraints.values():
constraint.modified = False
return solutionStatus
def actualResolve(self, lp, callback = None):
"""
Solve a well formulated lp problem
uses the old solver and modifies the rhs of the modified constraints
"""
log.debug("Resolve the Model using gurobi")
for constraint in lp.constraints.values():
if constraint.modified:
constraint.solverConstraint.setAttr(gurobipy.GRB.Attr.RHS,
-constraint.constant)
lp.solverModel.update()
self.callSolver(lp, callback = callback)
#get the solution information
solutionStatus = self.findSolutionValues(lp)
for var in lp.variables():
var.modified = False
for constraint in lp.constraints.values():
constraint.modified = False
return solutionStatus
class GUROBI_CMD(LpSolver_CMD):
"""The GUROBI_CMD solver"""
def defaultPath(self):
return self.executableExtension("gurobi_cl")
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)
if not self.keepFiles:
pid = os.getpid()
tmpLp = os.path.join(self.tmpDir, "%d-pulp.lp" % pid)
tmpSol = os.path.join(self.tmpDir, "%d-pulp.sol" % pid)
else:
tmpLp = lp.name+"-pulp.lp"
tmpSol = lp.name+"-pulp.sol"
lp.writeLP(tmpLp, writeSOS = 1)
try: os.remove(tmpSol)
except: pass
cmd = self.path
cmd += ' ' + ' '.join(['%s=%s' % (key, value)
for key, value in self.options])
cmd += ' ResultFile=%s' % tmpSol
if lp.isMIP():
if not self.mip:
warnings.warn('GUROBI_CMD does not allow a problem to be relaxed')
cmd += ' %s' % tmpLp
if self.msg:
pipe = None
else:
pipe = open(os.devnull, 'w')
return_code = subprocess.call(cmd.split(), stdout = pipe, stderr = pipe)
if return_code != 0:
raise PulpSolverError("PuLP: Error while trying to execute "+self.path)
if not self.keepFiles:
try: os.remove(tmpLp)
except: pass
if not os.path.exists(tmpSol):
warnings.warn('GUROBI_CMD does provide good solution status of non optimal solutions')
status = LpStatusNotSolved
else:
status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)
if not self.keepFiles:
try: os.remove(tmpSol)
except: pass
try: os.remove("gurobi.log")
except: pass
if status != LpStatusInfeasible:
lp.assignVarsVals(values)
lp.assignVarsDj(reducedCosts)
lp.assignConsPi(shadowPrices)
lp.assignConsSlack(slacks)
lp.status = status
return status
def readsol(self, filename):
"""Read a Gurobi solution file"""
with open(filename) as my_file:
try:
next(my_file) # skip the objective value
except StopIteration:
# Empty file not solved
warnings.warn('GUROBI_CMD does provide good solution status of non optimal solutions')
status = LpStatusNotSolved
return status, {}, {}, {}, {}
#We have no idea what the status is assume optimal
status = LpStatusOptimal
shadowPrices = {}
slacks = {}
shadowPrices = {}
slacks = {}
values = {}
reducedCosts = {}
for line in my_file:
name, value = line.split()
values[name] = float(value)
return status, values, reducedCosts, shadowPrices, slacks
#get the glpk name in global scope
glpk = None
class PYGLPK(LpSolver):
"""
The glpk LP/MIP solver (via its python interface)
Copyright Christophe-Marie Duquesne 2012
The glpk variables are available (after a solve) in var.solverVar
The glpk constraints are available in constraint.solverConstraint
The Model is in prob.solverModel
"""
try:
#import the model into the global scope
global glpk
import glpk.glpkpi as glpk
except:
def available(self):
"""True if the solver is available"""
return False
def actualSolve(self, lp, callback = None):
"""Solve a well formulated lp problem"""
raise PulpSolverError("GLPK: Not Available")
else:
def __init__(self,
mip = True,
msg = True,
timeLimit = None,
epgap = None,
**solverParams):
"""
Initializes the glpk solver.
@param mip: if False the solver will solve a MIP as an LP
@param msg: displays information from the solver to stdout
@param timeLimit: not handled
@param epgap: not handled
@param solverParams: not handled
"""
LpSolver.__init__(self, mip, msg)
if not self.msg:
glpk.glp_term_out(glpk.GLP_OFF)
def findSolutionValues(self, lp):
prob = lp.solverModel
if self.mip and self.hasMIPConstraints(lp.solverModel):
solutionStatus = glpk.glp_mip_status(prob)
else:
solutionStatus = glpk.glp_get_status(prob)
glpkLpStatus = {glpk.GLP_OPT: LpStatusOptimal,
glpk.GLP_UNDEF: LpStatusUndefined,
glpk.GLP_FEAS: LpStatusNotSolved,
glpk.GLP_INFEAS: LpStatusInfeasible,
glpk.GLP_NOFEAS: LpStatusInfeasible,
glpk.GLP_UNBND: LpStatusUnbounded
}
#populate pulp solution values
for var in lp.variables():
if self.mip and self.hasMIPConstraints(lp.solverModel):
var.varValue = glpk.glp_mip_col_val(prob, var.glpk_index)
else:
var.varValue = glpk.glp_get_col_prim(prob, var.glpk_index)
var.dj = glpk.glp_get_col_dual(prob, var.glpk_index)
#put pi and slack variables against the constraints
for constr in lp.constraints.values():
if self.mip and self.hasMIPConstraints(lp.solverModel):
row_val = glpk.glp_mip_row_val(prob, constr.glpk_index)
else:
row_val = glpk.glp_get_row_prim(prob, constr.glpk_index)
constr.slack = -constr.constant - row_val
constr.pi = glpk.glp_get_row_dual(prob, constr.glpk_index)
lp.resolveOK = True
for var in lp.variables():
var.isModified = False
lp.status = glpkLpStatus.get(solutionStatus,
LpStatusUndefined)
return lp.status
def available(self):
"""True if the solver is available"""
return True
def hasMIPConstraints(self, solverModel):
return (glpk.glp_get_num_int(solverModel) > 0 or
glpk.glp_get_num_bin(solverModel) > 0)
def callSolver(self, lp, callback = None):
"""Solves the problem with glpk
"""
self.solveTime = -clock()
glpk.glp_adv_basis(lp.solverModel, 0)
glpk.glp_simplex(lp.solverModel, None)
if self.mip and self.hasMIPConstraints(lp.solverModel):
status = glpk.glp_get_status(lp.solverModel)
if status in (glpk.GLP_OPT, glpk.GLP_UNDEF, glpk.GLP_FEAS):
glpk.glp_intopt(lp.solverModel, None)
self.solveTime += clock()
def buildSolverModel(self, lp):
"""
Takes the pulp lp model and translates it into a glpk model
"""
log.debug("create the glpk model")
prob = glpk.glp_create_prob()
glpk.glp_set_prob_name(prob, lp.name)
log.debug("set the sense of the problem")
if lp.sense == LpMaximize:
glpk.glp_set_obj_dir(prob, glpk.GLP_MAX)
log.debug("add the constraints to the problem")
glpk.glp_add_rows(prob, len(list(lp.constraints.keys())))
for i, v in enumerate(lp.constraints.items(), start=1):
name, constraint = v
glpk.glp_set_row_name(prob, i, name)
if constraint.sense == LpConstraintLE:
glpk.glp_set_row_bnds(prob, i, glpk.GLP_UP,
0.0, -constraint.constant)
elif constraint.sense == LpConstraintGE:
glpk.glp_set_row_bnds(prob, i, glpk.GLP_LO,
-constraint.constant, 0.0)
elif constraint.sense == LpConstraintEQ:
glpk.glp_set_row_bnds(prob, i, glpk.GLP_FX,
-constraint.constant, -constraint.constant)
else:
raise PulpSolverError('Detected an invalid constraint type')
constraint.glpk_index = i
log.debug("add the variables to the problem")
glpk.glp_add_cols(prob, len(lp.variables()))
for j, var in enumerate(lp.variables(), start=1):
glpk.glp_set_col_name(prob, j, var.name)
lb = 0.0
ub = 0.0
t = glpk.GLP_FR
if not var.lowBound is None:
lb = var.lowBound
t = glpk.GLP_LO
if not var.upBound is None:
ub = var.upBound
t = glpk.GLP_UP
if not var.upBound is None and not var.lowBound is None:
if ub == lb:
t = glpk.GLP_FX
else:
t = glpk.GLP_DB
glpk.glp_set_col_bnds(prob, j, t, lb, ub)
if var.cat == LpInteger:
glpk.glp_set_col_kind(prob, j, glpk.GLP_IV)
assert glpk.glp_get_col_kind(prob, j) == glpk.GLP_IV
var.glpk_index = j
log.debug("set the objective function")
for var in lp.variables():
value = lp.objective.get(var)
if value:
glpk.glp_set_obj_coef(prob, var.glpk_index, value)
log.debug("set the problem matrix")
for constraint in lp.constraints.values():
l = len(list(constraint.items()))
ind = glpk.intArray(l + 1)
val = glpk.doubleArray(l + 1)
for j, v in enumerate(constraint.items(), start=1):
var, value = v
ind[j] = var.glpk_index
val[j] = value
glpk.glp_set_mat_row(prob, constraint.glpk_index, l, ind,
val)
lp.solverModel = prob
#glpk.glp_write_lp(prob, None, "glpk.lp")
def actualSolve(self, lp, callback = None):
"""
Solve a well formulated lp problem
creates a glpk model, variables and constraints and attaches
them to the lp model which it then solves
"""
self.buildSolverModel(lp)
#set the initial solution
log.debug("Solve the Model using glpk")
self.callSolver(lp, callback = callback)
#get the solution information
solutionStatus = self.findSolutionValues(lp)
for var in lp.variables():
var.modified = False
for constraint in lp.constraints.values():
constraint.modified = False
return solutionStatus
def actualResolve(self, lp, callback = None):
"""
Solve a well formulated lp problem
uses the old solver and modifies the rhs of the modified
constraints
"""
log.debug("Resolve the Model using glpk")
for constraint in lp.constraints.values():
i = constraint.glpk_index
if constraint.modified:
if constraint.sense == LpConstraintLE:
glpk.glp_set_row_bnds(prob, i, glpk.GLP_UP,
0.0, -constraint.constant)
elif constraint.sense == LpConstraintGE:
glpk.glp_set_row_bnds(prob, i, glpk.GLP_LO,
-constraint.constant, 0.0)
elif constraint.sense == LpConstraintEQ:
glpk.glp_set_row_bnds(prob, i, glpk.GLP_FX,
-constraint.constant, -constraint.constant)
else:
raise PulpSolverError('Detected an invalid constraint type')
self.callSolver(lp, callback = callback)
#get the solution information
solutionStatus = self.findSolutionValues(lp)
for var in lp.variables():
var.modified = False
for constraint in lp.constraints.values():
constraint.modified = False
return solutionStatus
yaposib = None
class YAPOSIB(LpSolver):
"""
COIN OSI (via its python interface)
Copyright Christophe-Marie Duquesne 2012
The yaposib variables are available (after a solve) in var.solverVar
The yaposib constraints are available in constraint.solverConstraint
The Model is in prob.solverModel
"""
try:
#import the model into the global scope
global yaposib
import yaposib
except ImportError:
def available(self):
"""True if the solver is available"""
return False
def actualSolve(self, lp, callback = None):
"""Solve a well formulated lp problem"""
raise PulpSolverError("YAPOSIB: Not Available")
else:
def __init__(self,
mip = True,
msg = True,
timeLimit = None,
epgap = None,
solverName = None,
**solverParams):
"""
Initializes the yaposib solver.
@param mip: if False the solver will solve a MIP as
an LP
@param msg: displays information from the solver to
stdout
@param timeLimit: not supported
@param epgap: not supported
@param solverParams: not supported
"""
LpSolver.__init__(self, mip, msg)
if solverName:
self.solverName = solverName
else:
self.solverName = yaposib.available_solvers()[0]
def findSolutionValues(self, lp):
model = lp.solverModel
solutionStatus = model.status
yaposibLpStatus = {"optimal": LpStatusOptimal,
"undefined": LpStatusUndefined,
"abandoned": LpStatusInfeasible,
"infeasible": LpStatusInfeasible,
"limitreached": LpStatusInfeasible
}
#populate pulp solution values
for var in lp.variables():
var.varValue = var.solverVar.solution
var.dj = var.solverVar.reducedcost
#put pi and slack variables against the constraints
for constr in lp.constraints.values():
constr.pi = constr.solverConstraint.dual
constr.slack = -constr.constant - constr.solverConstraint.activity
if self.msg:
print("yaposib status=", solutionStatus)
lp.resolveOK = True
for var in lp.variables():
var.isModified = False
lp.status = yaposibLpStatus.get(solutionStatus,
LpStatusUndefined)
return lp.status
def available(self):
"""True if the solver is available"""
return True
def callSolver(self, lp, callback = None):
"""Solves the problem with yaposib
"""
if self.msg == 0:
#close stdout to get rid of messages
tempfile = open(mktemp(),'w')
savestdout = os.dup(1)
os.close(1)
if os.dup(tempfile.fileno()) != 1:
raise PulpSolverError("couldn't redirect stdout - dup() error")
self.solveTime = -clock()
lp.solverModel.solve(self.mip)
self.solveTime += clock()
if self.msg == 0:
#reopen stdout
os.close(1)
os.dup(savestdout)
os.close(savestdout)
def buildSolverModel(self, lp):
"""
Takes the pulp lp model and translates it into a yaposib model
"""
log.debug("create the yaposib model")
lp.solverModel = yaposib.Problem(self.solverName)
prob = lp.solverModel
prob.name = lp.name
log.debug("set the sense of the problem")
if lp.sense == LpMaximize:
prob.obj.maximize = True
log.debug("add the variables to the problem")
for var in lp.variables():
col = prob.cols.add(yaposib.vec([]))
col.name = var.name
if not var.lowBound is None:
col.lowerbound = var.lowBound
if not var.upBound is None:
col.upperbound = var.upBound
if var.cat == LpInteger:
col.integer = True
prob.obj[col.index] = lp.objective.get(var, 0.0)
var.solverVar = col
log.debug("add the Constraints to the problem")
for name, constraint in lp.constraints.items():
row = prob.rows.add(yaposib.vec([(var.solverVar.index,
value) for var, value in constraint.items()]))
if constraint.sense == LpConstraintLE:
row.upperbound = -constraint.constant
elif constraint.sense == LpConstraintGE:
row.lowerbound = -constraint.constant
elif constraint.sense == LpConstraintEQ:
row.upperbound = -constraint.constant
row.lowerbound = -constraint.constant
else:
raise PulpSolverError('Detected an invalid constraint type')
row.name = name
constraint.solverConstraint = row
def actualSolve(self, lp, callback = None):
"""
Solve a well formulated lp problem
creates a yaposib model, variables and constraints and attaches
them to the lp model which it then solves
"""
self.buildSolverModel(lp)
#set the initial solution
log.debug("Solve the model using yaposib")
self.callSolver(lp, callback = callback)
#get the solution information
solutionStatus = self.findSolutionValues(lp)
for var in lp.variables():
var.modified = False
for constraint in lp.constraints.values():
constraint.modified = False
return solutionStatus
def actualResolve(self, lp, callback = None):
"""
Solve a well formulated lp problem
uses the old solver and modifies the rhs of the modified
constraints
"""
log.debug("Resolve the model using yaposib")
for constraint in lp.constraints.values():
row = constraint.solverConstraint
if constraint.modified:
if constraint.sense == LpConstraintLE:
row.upperbound = -constraint.constant
elif constraint.sense == LpConstraintGE:
row.lowerbound = -constraint.constant
elif constraint.sense == LpConstraintEQ:
row.upperbound = -constraint.constant
row.lowerbound = -constraint.constant
else:
raise PulpSolverError('Detected an invalid constraint type')
self.callSolver(lp, callback = callback)
#get the solution information
solutionStatus = self.findSolutionValues(lp)
for var in lp.variables():
var.modified = False
for constraint in lp.constraints.values():
constraint.modified = False
return solutionStatus
try:
import ctypes
def ctypesArrayFill(myList, type=ctypes.c_double):
"""
Creates a c array with ctypes from a python list
type is the type of the c array
"""
ctype= type * len(myList)
cList = ctype()
for i,elem in enumerate(myList):
cList[i] = elem
return cList
except(ImportError):
def ctypesArrayFill(myList, type = None):
return None
class GurobiFormulation(object):
"""
The Gurobi LP/MIP solver (via its python interface)
without holding our own copy of the constraints
Contributed by Ben Hollis<ben.hollis@polymathian.com>
This is an experimental interface that implements some of the
LpProblem interface, this should probably be done with an ABC
Also needs tests
"""
try:
sys.path.append(gurobi_path)
global gurobipy
import gurobipy
except:
def __init__(self, sense):
raise PulpSolverError("GUROBI: Not Available")
else:
def __init__(self, name, sense):
self.gurobi_model = gurobipy.Model(name)
self.sense = sense
if sense == LpMaximize:
self.gurobi_model.setAttr("ModelSense", -1)
self.varables = {}
self.objective = None
self.status = None
def addVariable(self, v):
if v.name not in self.varables:
self.varables[v.name] = v
lower_bound = v.getLb()
if lower_bound is None:
lower_bound = -gurobipy.GRB.INFINITY
upper_bound = v.getUb()
if upper_bound is None:
upper_bound = gurobipy.GRB.INFINITY
varType = gurobipy.GRB.CONTINUOUS
if v.isInteger():
varType = gurobipy.GRB.INTEGER
v.solver_var = self.gurobi_model.addVar(lower_bound, upper_bound, vtype = varType, obj = 0, name = v.name)
return v
def update(self):
self.gurobi_model.update()
def numVariables(self):
return self.gurobi_model.getAttr('NumVars')
def numConstraints(self):
return self.gurobi_model.getAttr('NumConstrs')
def getSense(self):
return self.sense
def addVariables(self, variables):
[self.addVariable(v) for v in variables]
def add(self, constraint, name = None):
self.addConstraint(constraint, name)
def solve(self, callback = None):
print("***Solving using thin Gurobi Formulation")
self.gurobi_model.reset()
for var, coeff in self.objective.items():
var.solver_var.setAttr("Obj", coeff)
self.gurobi_model.optimize(callback = callback)
return self.findSolutionValues()
def findSolutionValues(self):
for var in self.varables.values():
try:
var.varValue = var.solver_var.X
except gurobipy.GurobiError:
pass
GRB = gurobipy.GRB
gurobiLPStatus = {
GRB.OPTIMAL: LpStatusOptimal,
GRB.INFEASIBLE: LpStatusInfeasible,
GRB.INF_OR_UNBD: LpStatusInfeasible,
GRB.UNBOUNDED: LpStatusUnbounded,
GRB.ITERATION_LIMIT: LpStatusNotSolved,
GRB.NODE_LIMIT: LpStatusNotSolved,
GRB.TIME_LIMIT: LpStatusNotSolved,
GRB.SOLUTION_LIMIT: LpStatusNotSolved,
GRB.INTERRUPTED: LpStatusNotSolved,
GRB.NUMERIC: LpStatusNotSolved,
}
self.status = gurobiLPStatus.get(self.gurobi_model.Status, LpStatusUndefined)
return self.status
def addConstraint(self, constraint, name = None):
if not isinstance(constraint, LpConstraint):
raise TypeError("Can only add LpConstraint objects")
if name:
constraint.name = name
try:
if constraint.name:
name = constraint.name
else:
name = self.unusedConstraintName()
except AttributeError:
raise TypeError("Can only add LpConstraint objects")
#if self._addVariables(constraint.keys()):
#self.gurobi_model.update()
expr = gurobipy.LinExpr(constraint.values(), [v.solver_var for v in constraint.keys()]) # Solver_var is added inside addVariable
if constraint.sense == LpConstraintLE:
relation = gurobipy.GRB.LESS_EQUAL
elif constraint.sense == LpConstraintGE:
relation = gurobipy.GRB.GREATER_EQUAL
elif constraint.sense == LpConstraintEQ:
relation = gurobipy.GRB.EQUAL
else:
raise PulpSolverError('Detected an invalid constraint type')
self.gurobi_model.addConstr(expr, relation, -constraint.constant, name)
def __iadd__(self, other):
if isinstance(other, tuple):
other, name = other
else:
name = None
if other is True:
return self
elif isinstance(other, LpConstraint):
self.addConstraint(other, name)
elif isinstance(other, LpAffineExpression):
self.objective = other
self.objective.name = name
elif isinstance(other, LpVariable) or isinstance(other, (int, float)):
self.objective = LpAffineExpression(other)
self.objective.name = name
else:
raise TypeError("Can only add LpConstraint, LpAffineExpression or True objects")
return self
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