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stringlengths 2
98
⌀ | name
stringlengths 1
76
| docstring
stringlengths 0
281k
⌀ | code
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38,032 | pulp.apis.coin_api | writesol |
Writes a CBC solution file generated from an mps / lp file (possible different names)
returns True on success
| def writesol(self, filename, lp, vs, variablesNames, constraintsNames):
"""
Writes a CBC solution file generated from an mps / lp file (possible different names)
returns True on success
"""
values = {v.name: v.value() if v.value() is not None else 0 for v in vs}
value_lines = []
value_lines += [
(i, v, values[k], 0) for i, (k, v) in enumerate(variablesNames.items())
]
lines = ["Stopped on time - objective value 0\n"]
lines += ["{:>7} {} {:>15} {:>23}\n".format(*tup) for tup in value_lines]
with open(filename, "w") as f:
f.writelines(lines)
return True
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|
38,033 | pulp.apis.coin_api | COINMP_DLL |
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
| 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
"""
name = "COINMP_DLL"
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,
cuts=1,
presolve=1,
dual=1,
crash=0,
scale=1,
rounding=1,
integerPresolve=1,
strong=5,
*args,
**kwargs,
):
LpSolver.__init__(self, *args, **kwargs)
self.fracGap = None
gapRel = self.optionsDict.get("gapRel")
if gapRel is not None:
self.fracGap = float(gapRel)
if self.timeLimit is not None:
self.timeLimit = float(self.timeLimit)
# 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(self)
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
self.lib.CoinSetIntOption(
hProb, self.COIN_INT_LOGLEVEL, ctypes.c_int(self.msg)
)
if self.timeLimit:
if self.mip:
self.lib.CoinSetRealOption(
hProb, self.COIN_REAL_MIPMAXSEC, ctypes.c_double(self.timeLimit)
)
else:
self.lib.CoinSetRealOption(
hProb,
self.COIN_REAL_MAXSECONDS,
ctypes.c_double(self.timeLimit),
)
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:
self.lib.CoinRegisterMsgLogCallback(
hProb, ctypes.c_char_p(""), ctypes.POINTER(ctypes.c_int)()
)
self.coinTime = -clock()
self.lib.CoinOptimizeProblem(hProb, 0)
self.coinTime += clock()
# TODO: check Integer Feasible status
CoinLpStatus = {
0: constants.LpStatusOptimal,
1: constants.LpStatusInfeasible,
2: constants.LpStatusInfeasible,
3: constants.LpStatusNotSolved,
4: constants.LpStatusNotSolved,
5: constants.LpStatusNotSolved,
-1: constants.LpStatusUndefined,
}
solutionStatus = self.lib.CoinGetSolutionStatus(hProb)
solutionText = self.lib.CoinGetSolutionText(hProb)
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]] = cSlackValues[i]
lp.assignConsPi(constraintpivalues)
lp.assignConsSlack(constraintslackvalues)
self.lib.CoinFreeSolver()
status = CoinLpStatus[self.lib.CoinGetSolutionStatus(hProb)]
lp.assignStatus(status)
return status
| (mip=True, msg=True, options=None, timeLimit=None, *args, **kwargs) | [
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|
38,034 | pulp.apis.core | __init__ |
:param bool mip: if False, assume LP even if integer variables
:param bool msg: if False, no log is shown
:param list options:
:param float timeLimit: maximum time for solver (in seconds)
:param args:
:param kwargs: optional named options to pass to each solver,
e.g. gapRel=0.1, gapAbs=10, logPath="",
| def __init__(
self, mip=True, msg=True, options=None, timeLimit=None, *args, **kwargs
):
"""
:param bool mip: if False, assume LP even if integer variables
:param bool msg: if False, no log is shown
:param list options:
:param float timeLimit: maximum time for solver (in seconds)
:param args:
:param kwargs: optional named options to pass to each solver,
e.g. gapRel=0.1, gapAbs=10, logPath="",
"""
if options is None:
options = []
self.mip = mip
self.msg = msg
self.options = options
self.timeLimit = timeLimit
# here we will store all other relevant information including:
# gapRel, gapAbs, maxMemory, maxNodes, threads, logPath, timeMode
self.optionsDict = {k: v for k, v in kwargs.items() if v is not None}
| (self, mip=True, msg=True, options=None, timeLimit=None, *args, **kwargs) | [
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]
|
38,036 | pulp.apis.coin_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
raise PulpSolverError("COINMP_DLL: Not Available")
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|
38,037 | pulp.apis.core | copy | Make a copy of self | 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
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|
38,044 | pulp.apis.coin_api | COINMP_DLL_load_dll |
function that loads the DLL useful for debugging installation problems
| def COINMP_DLL_load_dll(path):
"""
function that loads the DLL useful for debugging installation problems
"""
if os.name == "nt":
lib = ctypes.windll.LoadLibrary(str(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
| (path) | [
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|
38,070 | pulp.apis.copt_api | COPT |
The COPT Optimizer via its python interface
The COPT variables are available (after a solve) in var.solverVar
Constraints in constraint.solverConstraint
and the Model is in prob.solverModel
| class COPT(LpSolver):
"""
The COPT Optimizer via its python interface
The COPT variables are available (after a solve) in var.solverVar
Constraints in constraint.solverConstraint
and the Model is in prob.solverModel
"""
name = "COPT"
try:
global coptpy
import coptpy
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("COPT: Not available")
else:
def __init__(
self,
mip=True,
msg=True,
timeLimit=None,
gapRel=None,
warmStart=False,
logPath=None,
**solverParams,
):
"""
: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 float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param bool warmStart: if True, the solver will use the current value of variables as a start
:param str logPath: path to the log file
"""
LpSolver.__init__(
self,
mip=mip,
msg=msg,
timeLimit=timeLimit,
gapRel=gapRel,
logPath=logPath,
warmStart=warmStart,
)
self.coptenv = coptpy.Envr()
self.coptmdl = self.coptenv.createModel()
if not self.msg:
self.coptmdl.setParam("Logging", 0)
for key, value in solverParams.items():
self.coptmdl.setParam(key, value)
def findSolutionValues(self, lp):
model = lp.solverModel
solutionStatus = model.status
CoptLpStatus = {
coptpy.COPT.UNSTARTED: LpStatusNotSolved,
coptpy.COPT.OPTIMAL: LpStatusOptimal,
coptpy.COPT.INFEASIBLE: LpStatusInfeasible,
coptpy.COPT.UNBOUNDED: LpStatusUnbounded,
coptpy.COPT.INF_OR_UNB: LpStatusInfeasible,
coptpy.COPT.NUMERICAL: LpStatusNotSolved,
coptpy.COPT.NODELIMIT: LpStatusNotSolved,
coptpy.COPT.TIMEOUT: LpStatusNotSolved,
coptpy.COPT.UNFINISHED: LpStatusNotSolved,
coptpy.COPT.INTERRUPTED: LpStatusNotSolved,
}
if self.msg:
print("COPT status=", solutionStatus)
lp.resolveOK = True
for var in lp._variables:
var.isModified = False
status = CoptLpStatus.get(solutionStatus, LpStatusUndefined)
lp.assignStatus(status)
if status != LpStatusOptimal:
return status
values = model.getInfo("Value", model.getVars())
for var, value in zip(lp._variables, values):
var.varValue = value
if not model.ismip:
# NOTE: slacks in COPT are activities of rows
slacks = model.getInfo("Slack", model.getConstrs())
for constr, value in zip(lp.constraints.values(), slacks):
constr.slack = value
redcosts = model.getInfo("RedCost", model.getVars())
for var, value in zip(lp._variables, redcosts):
var.dj = value
duals = model.getInfo("Dual", model.getConstrs())
for constr, value in zip(lp.constraints.values(), duals):
constr.pi = value
return status
def available(self):
"""True if the solver is available"""
return True
def callSolver(self, lp, callback=None):
"""Solves the problem with COPT"""
self.solveTime = -clock()
if callback is not None:
lp.solverModel.setCallback(
callback,
coptpy.COPT.CBCONTEXT_MIPRELAX | coptpy.COPT.CBCONTEXT_MIPSOL,
)
lp.solverModel.solve()
self.solveTime += clock()
def buildSolverModel(self, lp):
"""
Takes the pulp lp model and translates it into a COPT model
"""
lp.solverModel = self.coptmdl
if lp.sense == LpMaximize:
lp.solverModel.objsense = coptpy.COPT.MAXIMIZE
if self.timeLimit:
lp.solverModel.setParam("TimeLimit", self.timeLimit)
gapRel = self.optionsDict.get("gapRel")
logPath = self.optionsDict.get("logPath")
if gapRel:
lp.solverModel.setParam("RelGap", gapRel)
if logPath:
lp.solverModel.setLogFile(logPath)
for var in lp.variables():
lowBound = var.lowBound
if lowBound is None:
lowBound = -coptpy.COPT.INFINITY
upBound = var.upBound
if upBound is None:
upBound = coptpy.COPT.INFINITY
obj = lp.objective.get(var, 0.0)
varType = coptpy.COPT.CONTINUOUS
if var.cat == LpInteger and self.mip:
varType = coptpy.COPT.INTEGER
var.solverVar = lp.solverModel.addVar(
lowBound, upBound, vtype=varType, obj=obj, name=var.name
)
if self.optionsDict.get("warmStart", False):
for var in lp._variables:
if var.varValue is not None:
self.coptmdl.setMipStart(var.solverVar, var.varValue)
self.coptmdl.loadMipStart()
for name, constraint in lp.constraints.items():
expr = coptpy.LinExpr(
[v.solverVar for v in constraint.keys()], list(constraint.values())
)
if constraint.sense == LpConstraintLE:
relation = coptpy.COPT.LESS_EQUAL
elif constraint.sense == LpConstraintGE:
relation = coptpy.COPT.GREATER_EQUAL
elif constraint.sense == LpConstraintEQ:
relation = coptpy.COPT.EQUAL
else:
raise PulpSolverError("Detected an invalid constraint type")
constraint.solverConstraint = lp.solverModel.addConstr(
expr, relation, -constraint.constant, name
)
def actualSolve(self, lp, callback=None):
"""
Solve a well formulated lp problem
creates a COPT model, variables and constraints and attaches
them to the lp model which it then solves
"""
self.buildSolverModel(lp)
self.callSolver(lp, callback=callback)
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
"""
for constraint in lp.constraints.values():
if constraint.modified:
if constraint.sense == LpConstraintLE:
constraint.solverConstraint.ub = -constraint.constant
elif constraint.sense == LpConstraintGE:
constraint.solverConstraint.lb = -constraint.constant
else:
constraint.solverConstraint.lb = -constraint.constant
constraint.solverConstraint.ub = -constraint.constant
self.callSolver(lp, callback=callback)
solutionStatus = self.findSolutionValues(lp)
for var in lp. | (mip=True, msg=True, options=None, timeLimit=None, *args, **kwargs) | [
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|
38,073 | pulp.apis.copt_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp, callback=None):
"""Solve a well formulated lp problem"""
raise PulpSolverError("COPT: Not available")
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|
38,074 | pulp.apis.copt_api | available | True if the solver is available | def available(self):
"""True if the solver is available"""
return False
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|
38,082 | pulp.apis.copt_api | COPT_CMD |
The COPT command-line solver
| class COPT_CMD(LpSolver_CMD):
"""
The COPT command-line solver
"""
name = "COPT_CMD"
def __init__(
self,
path=None,
keepFiles=0,
mip=True,
msg=True,
mip_start=False,
warmStart=False,
logfile=None,
**params,
):
"""
Initialize command-line solver
"""
LpSolver_CMD.__init__(self, path, keepFiles, mip, msg, [])
self.mipstart = warmStart
self.logfile = logfile
self.solverparams = params
def defaultPath(self):
"""
The default path of 'copt_cmd'
"""
return self.executableExtension("copt_cmd")
def available(self):
"""
True if 'copt_cmd' is available
"""
return self.executable(self.path)
def actualSolve(self, lp):
"""
Solve a well formulated LP problem
This function borrowed implementation of CPLEX_CMD.actualSolve and
GUROBI_CMD.actualSolve, with some modifications.
"""
if not self.available():
raise PulpSolverError("COPT_PULP: Failed to execute '{}'".format(self.path))
if not self.keepFiles:
uuid = uuid4().hex
tmpLp = os.path.join(self.tmpDir, "{}-pulp.lp".format(uuid))
tmpSol = os.path.join(self.tmpDir, "{}-pulp.sol".format(uuid))
tmpMst = os.path.join(self.tmpDir, "{}-pulp.mst".format(uuid))
else:
# Replace space with underscore to make filepath better
tmpName = lp.name
tmpName = tmpName.replace(" ", "_")
tmpLp = tmpName + "-pulp.lp"
tmpSol = tmpName + "-pulp.sol"
tmpMst = tmpName + "-pulp.mst"
lpvars = lp.writeLP(tmpLp, writeSOS=1)
# Generate solving commands
solvecmds = self.path
solvecmds += " -c "
solvecmds += '"read ' + tmpLp + ";"
if lp.isMIP() and self.mipstart:
self.writemst(tmpMst, lpvars)
solvecmds += "read " + tmpMst + ";"
if self.logfile is not None:
solvecmds += "set logfile {};".format(self.logfile)
if self.solverparams is not None:
for parname, parval in self.solverparams.items():
solvecmds += "set {0} {1};".format(parname, parval)
if lp.isMIP() and not self.mip:
solvecmds += "optimizelp;"
else:
solvecmds += "optimize;"
solvecmds += "write " + tmpSol + ";"
solvecmds += 'exit"'
try:
os.remove(tmpSol)
except:
pass
if self.msg:
msgpipe = None
else:
msgpipe = open(os.devnull, "w")
rc = subprocess.call(solvecmds, shell=True, stdout=msgpipe, stderr=msgpipe)
if msgpipe is not None:
msgpipe.close()
# Get and analyze result
if rc != 0:
raise PulpSolverError("COPT_PULP: Failed to execute '{}'".format(self.path))
if not os.path.exists(tmpSol):
status = LpStatusNotSolved
else:
status, values = self.readsol(tmpSol)
if not self.keepFiles:
for oldfile in [tmpLp, tmpSol, tmpMst]:
try:
os.remove(oldfile)
except:
pass
if status == LpStatusOptimal:
lp.assignVarsVals(values)
# lp.assignStatus(status)
lp.status = status
return status
def readsol(self, filename):
"""
Read COPT solution file
"""
with open(filename) as solfile:
try:
next(solfile)
except StopIteration:
warnings.warn("COPT_PULP: No solution was returned")
return LpStatusNotSolved, {}
# TODO: No information about status, assumed to be optimal
status = LpStatusOptimal
values = {}
for line in solfile:
if line[0] != "#":
varname, varval = line.split()
values[varname] = float(varval)
return status, values
def writemst(self, filename, lpvars):
"""
Write COPT MIP start file
"""
mstvals = [(v.name, v.value()) for v in lpvars if v.value() is not None]
mstline = []
for varname, varval in mstvals:
mstline.append("{0} {1}".format(varname, varval))
with open(filename, "w") as mstfile:
mstfile.write("\n".join(mstline))
return True
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|
38,083 | pulp.apis.copt_api | __init__ |
Initialize command-line solver
| def __init__(
self,
path=None,
keepFiles=0,
mip=True,
msg=True,
mip_start=False,
warmStart=False,
logfile=None,
**params,
):
"""
Initialize command-line solver
"""
LpSolver_CMD.__init__(self, path, keepFiles, mip, msg, [])
self.mipstart = warmStart
self.logfile = logfile
self.solverparams = params
| (self, path=None, keepFiles=0, mip=True, msg=True, mip_start=False, warmStart=False, logfile=None, **params) | [
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]
|
38,085 | pulp.apis.copt_api | actualSolve |
Solve a well formulated LP problem
This function borrowed implementation of CPLEX_CMD.actualSolve and
GUROBI_CMD.actualSolve, with some modifications.
| def actualSolve(self, lp):
"""
Solve a well formulated LP problem
This function borrowed implementation of CPLEX_CMD.actualSolve and
GUROBI_CMD.actualSolve, with some modifications.
"""
if not self.available():
raise PulpSolverError("COPT_PULP: Failed to execute '{}'".format(self.path))
if not self.keepFiles:
uuid = uuid4().hex
tmpLp = os.path.join(self.tmpDir, "{}-pulp.lp".format(uuid))
tmpSol = os.path.join(self.tmpDir, "{}-pulp.sol".format(uuid))
tmpMst = os.path.join(self.tmpDir, "{}-pulp.mst".format(uuid))
else:
# Replace space with underscore to make filepath better
tmpName = lp.name
tmpName = tmpName.replace(" ", "_")
tmpLp = tmpName + "-pulp.lp"
tmpSol = tmpName + "-pulp.sol"
tmpMst = tmpName + "-pulp.mst"
lpvars = lp.writeLP(tmpLp, writeSOS=1)
# Generate solving commands
solvecmds = self.path
solvecmds += " -c "
solvecmds += '"read ' + tmpLp + ";"
if lp.isMIP() and self.mipstart:
self.writemst(tmpMst, lpvars)
solvecmds += "read " + tmpMst + ";"
if self.logfile is not None:
solvecmds += "set logfile {};".format(self.logfile)
if self.solverparams is not None:
for parname, parval in self.solverparams.items():
solvecmds += "set {0} {1};".format(parname, parval)
if lp.isMIP() and not self.mip:
solvecmds += "optimizelp;"
else:
solvecmds += "optimize;"
solvecmds += "write " + tmpSol + ";"
solvecmds += 'exit"'
try:
os.remove(tmpSol)
except:
pass
if self.msg:
msgpipe = None
else:
msgpipe = open(os.devnull, "w")
rc = subprocess.call(solvecmds, shell=True, stdout=msgpipe, stderr=msgpipe)
if msgpipe is not None:
msgpipe.close()
# Get and analyze result
if rc != 0:
raise PulpSolverError("COPT_PULP: Failed to execute '{}'".format(self.path))
if not os.path.exists(tmpSol):
status = LpStatusNotSolved
else:
status, values = self.readsol(tmpSol)
if not self.keepFiles:
for oldfile in [tmpLp, tmpSol, tmpMst]:
try:
os.remove(oldfile)
except:
pass
if status == LpStatusOptimal:
lp.assignVarsVals(values)
# lp.assignStatus(status)
lp.status = status
return status
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|
38,086 | pulp.apis.copt_api | available |
True if 'copt_cmd' is available
| def available(self):
"""
True if 'copt_cmd' is available
"""
return self.executable(self.path)
| (self) | [
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|
38,089 | pulp.apis.copt_api | defaultPath |
The default path of 'copt_cmd'
| def defaultPath(self):
"""
The default path of 'copt_cmd'
"""
return self.executableExtension("copt_cmd")
| (self) | [
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]
|
38,094 | pulp.apis.copt_api | readsol |
Read COPT solution file
| def readsol(self, filename):
"""
Read COPT solution file
"""
with open(filename) as solfile:
try:
next(solfile)
except StopIteration:
warnings.warn("COPT_PULP: No solution was returned")
return LpStatusNotSolved, {}
# TODO: No information about status, assumed to be optimal
status = LpStatusOptimal
values = {}
for line in solfile:
if line[0] != "#":
varname, varval = line.split()
values[varname] = float(varval)
return status, values
| (self, filename) | [
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|
38,102 | pulp.apis.copt_api | writemst |
Write COPT MIP start file
| def writemst(self, filename, lpvars):
"""
Write COPT MIP start file
"""
mstvals = [(v.name, v.value()) for v in lpvars if v.value() is not None]
mstline = []
for varname, varval in mstvals:
mstline.append("{0} {1}".format(varname, varval))
with open(filename, "w") as mstfile:
mstfile.write("\n".join(mstline))
return True
| (self, filename, lpvars) | [
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|
38,103 | pulp.apis.copt_api | COPT_DLL |
The COPT dynamic library solver
| class COPT_DLL(LpSolver):
"""
The COPT dynamic library solver
"""
name = "COPT_DLL"
try:
coptlib = COPT_DLL_loadlib()
except Exception as e:
err = e
"""The COPT dynamic library solver (DLL). 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(f"COPT_DLL: Not Available:\n{self.err}")
else:
# COPT API name map
CreateEnv = coptlib.COPT_CreateEnv
DeleteEnv = coptlib.COPT_DeleteEnv
CreateProb = coptlib.COPT_CreateProb
DeleteProb = coptlib.COPT_DeleteProb
LoadProb = coptlib.COPT_LoadProb
AddCols = coptlib.COPT_AddCols
WriteMps = coptlib.COPT_WriteMps
WriteLp = coptlib.COPT_WriteLp
WriteBin = coptlib.COPT_WriteBin
WriteSol = coptlib.COPT_WriteSol
WriteBasis = coptlib.COPT_WriteBasis
WriteMst = coptlib.COPT_WriteMst
WriteParam = coptlib.COPT_WriteParam
AddMipStart = coptlib.COPT_AddMipStart
SolveLp = coptlib.COPT_SolveLp
Solve = coptlib.COPT_Solve
GetSolution = coptlib.COPT_GetSolution
GetLpSolution = coptlib.COPT_GetLpSolution
GetIntParam = coptlib.COPT_GetIntParam
SetIntParam = coptlib.COPT_SetIntParam
GetDblParam = coptlib.COPT_GetDblParam
SetDblParam = coptlib.COPT_SetDblParam
GetIntAttr = coptlib.COPT_GetIntAttr
GetDblAttr = coptlib.COPT_GetDblAttr
SearchParamAttr = coptlib.COPT_SearchParamAttr
SetLogFile = coptlib.COPT_SetLogFile
def __init__(
self,
mip=True,
msg=True,
mip_start=False,
warmStart=False,
logfile=None,
**params,
):
"""
Initialize COPT solver
"""
LpSolver.__init__(self, mip, msg)
# Initialize COPT environment and problem
self.coptenv = None
self.coptprob = None
# Use MIP start information
self.mipstart = warmStart
# Create COPT environment and problem
self.create()
# Set log file
if logfile is not None:
rc = self.SetLogFile(self.coptprob, coptstr(logfile))
if rc != 0:
raise PulpSolverError("COPT_PULP: Failed to set log file")
# Set parameters to problem
if not self.msg:
self.setParam("Logging", 0)
for parname, parval in params.items():
self.setParam(parname, parval)
def available(self):
"""
True if dynamic library is available
"""
return True
def actualSolve(self, lp):
"""
Solve a well formulated LP/MIP problem
This function borrowed implementation of CPLEX_DLL.actualSolve,
with some modifications.
"""
# Extract problem data and load it into COPT
(
ncol,
nrow,
nnonz,
objsen,
objconst,
colcost,
colbeg,
colcnt,
colind,
colval,
coltype,
collb,
colub,
rowsense,
rowrhs,
colname,
rowname,
) = self.extract(lp)
rc = self.LoadProb(
self.coptprob,
ncol,
nrow,
objsen,
objconst,
colcost,
colbeg,
colcnt,
colind,
colval,
coltype,
collb,
colub,
rowsense,
rowrhs,
None,
colname,
rowname,
)
if rc != 0:
raise PulpSolverError("COPT_PULP: Failed to load problem")
if lp.isMIP() and self.mip:
# Load MIP start information
if self.mipstart:
mstdict = {
self.v2n[v]: v.value()
for v in lp.variables()
if v.value() is not None
}
if mstdict:
mstkeys = ctypesArrayFill(list(mstdict.keys()), ctypes.c_int)
mstvals = ctypesArrayFill(
list(mstdict.values()), ctypes.c_double
)
rc = self.AddMipStart(
self.coptprob, len(mstkeys), mstkeys, mstvals
)
if rc != 0:
raise PulpSolverError(
"COPT_PULP: Failed to add MIP start information"
)
# Solve the problem
rc = self.Solve(self.coptprob)
if rc != 0:
raise PulpSolverError("COPT_PULP: Failed to solve the MIP problem")
elif lp.isMIP() and not self.mip:
# Solve MIP as LP
rc = self.SolveLp(self.coptprob)
if rc != 0:
raise PulpSolverError("COPT_PULP: Failed to solve MIP as LP")
else:
# Solve the LP problem
rc = self.SolveLp(self.coptprob)
if rc != 0:
raise PulpSolverError("COPT_PULP: Failed to solve the LP problem")
# Get problem status and solution
status = self.getsolution(lp, ncol, nrow)
# Reset attributes
for var in lp.variables():
var.modified = False
return status
def extract(self, lp):
"""
Extract data from PuLP lp structure
This function borrowed implementation of LpSolver.getCplexStyleArrays,
with some modifications.
"""
cols = list(lp.variables())
ncol = len(cols)
nrow = len(lp.constraints)
collb = (ctypes.c_double * ncol)()
colub = (ctypes.c_double * ncol)()
colcost = (ctypes.c_double * ncol)()
coltype = (ctypes.c_char * ncol)()
colname = (ctypes.c_char_p * ncol)()
rowrhs = (ctypes.c_double * nrow)()
rowsense = (ctypes.c_char * nrow)()
rowname = (ctypes.c_char_p * nrow)()
spmat = sparse.Matrix(list(range(nrow)), list(range(ncol)))
# Objective sense and constant offset
objsen = coptobjsen[lp.sense]
objconst = ctypes.c_double(0.0)
# Associate each variable with a ordinal
self.v2n = dict(((cols[i], i) for i in range(ncol)))
self.vname2n = dict(((cols[i].name, i) for i in range(ncol)))
self.n2v = dict((i, cols[i]) for i in range(ncol))
self.c2n = {}
self.n2c = {}
self.addedVars = ncol
self.addedRows = nrow
# Extract objective cost
for col, val in lp.objective.items():
colcost[self.v2n[col]] = val
# Extract variable types, names and lower/upper bounds
for col in lp.variables():
colname[self.v2n[col]] = coptstr(col.name)
if col.lowBound is not None:
collb[self.v2n[col]] = col.lowBound
else:
collb[self.v2n[col]] = -1e30
if col.upBound is not None:
colub[self.v2n[col]] = col.upBound
else:
colub[self.v2n[col]] = 1e30
# Extract column types
if lp.isMIP():
for var in lp.variables():
coltype[self.v2n[var]] = coptctype[var.cat]
| (mip=True, msg=True, options=None, timeLimit=None, *args, **kwargs) | [
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|
38,106 | pulp.apis.copt_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
raise PulpSolverError(f"COPT_DLL: Not Available:\n{self.err}")
| (self, lp) | [
0.002041481202468276,
-0.03737911209464073,
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0.09630455821752548,
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|
38,115 | pulp.apis.copt_api | COPT_DLL_loadlib |
Load COPT shared library in all supported platforms
| def COPT_DLL_loadlib():
"""
Load COPT shared library in all supported platforms
"""
from glob import glob
libfile = None
libpath = None
libhome = os.getenv("COPT_HOME")
if sys.platform == "win32":
libfile = glob(os.path.join(libhome, "bin", "copt.dll"))
elif sys.platform == "linux":
libfile = glob(os.path.join(libhome, "lib", "libcopt.so"))
elif sys.platform == "darwin":
libfile = glob(os.path.join(libhome, "lib", "libcopt.dylib"))
else:
raise PulpSolverError("COPT_PULP: Unsupported operating system")
# Find desired library in given search path
if libfile:
libpath = libfile[0]
if libpath is None:
raise PulpSolverError(
"COPT_PULP: Failed to locate solver library, "
"please refer to COPT manual for installation guide"
)
else:
if sys.platform == "win32":
coptlib = ctypes.windll.LoadLibrary(libpath)
else:
coptlib = ctypes.cdll.LoadLibrary(libpath)
return coptlib
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|
38,116 | pulp.apis.cplex_api | CPLEX_CMD | The CPLEX LP solver | class CPLEX_CMD(LpSolver_CMD):
"""The CPLEX LP solver"""
name = "CPLEX_CMD"
def __init__(
self,
mip=True,
msg=True,
timeLimit=None,
gapRel=None,
gapAbs=None,
options=None,
warmStart=False,
keepFiles=False,
path=None,
threads=None,
logPath=None,
maxMemory=None,
maxNodes=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 float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param float gapAbs: absolute gap tolerance for the solver to stop
:param int threads: sets the maximum number of threads
:param list options: list of additional options to pass to solver
:param bool warmStart: if True, the solver will use the current value of variables as a start
: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
:param str logPath: path to the log file
:param float maxMemory: max memory to use during the solving. Stops the solving when reached.
:param int maxNodes: max number of nodes during branching. Stops the solving when reached.
"""
LpSolver_CMD.__init__(
self,
gapRel=gapRel,
mip=mip,
msg=msg,
timeLimit=timeLimit,
options=options,
maxMemory=maxMemory,
maxNodes=maxNodes,
warmStart=warmStart,
path=path,
keepFiles=keepFiles,
threads=threads,
gapAbs=gapAbs,
logPath=logPath,
)
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)
tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, "lp", "sol", "mst")
vs = 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.optionsDict.get("warmStart", False):
self.writesol(filename=tmpMst, vs=vs)
cplex_cmds += "read " + tmpMst + "\n"
cplex_cmds += "set advance 1\n"
if self.timeLimit is not None:
cplex_cmds += "set timelimit " + str(self.timeLimit) + "\n"
options = self.options + self.getOptions()
for option in 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 os.path.exists(tmpSol):
status = constants.LpStatusInfeasible
values = reducedCosts = shadowPrices = slacks = solStatus = None
else:
(
status,
values,
reducedCosts,
shadowPrices,
slacks,
solStatus,
) = self.readsol(tmpSol)
self.delete_tmp_files(tmpLp, tmpMst, tmpSol)
if self.optionsDict.get("logPath") != "cplex.log":
self.delete_tmp_files("cplex.log")
if status != constants.LpStatusInfeasible:
lp.assignVarsVals(values)
lp.assignVarsDj(reducedCosts)
lp.assignConsPi(shadowPrices)
lp.assignConsSlack(slacks)
lp.assignStatus(status, solStatus)
return status
def getOptions(self):
# CPLEX parameters: https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.6.0/ilog.odms.cplex.help/CPLEX/GettingStarted/topics/tutorials/InteractiveOptimizer/settingParams.html
# CPLEX status: https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.10.0/ilog.odms.cplex.help/refcallablelibrary/macros/Solution_status_codes.html
params_eq = dict(
logPath="set logFile {}",
gapRel="set mip tolerances mipgap {}",
gapAbs="set mip tolerances absmipgap {}",
maxMemory="set mip limits treememory {}",
threads="set threads {}",
maxNodes="set mip limits nodes {}",
)
return [
v.format(self.optionsDict[k])
for k, v in params_eq.items()
if k in self.optionsDict and self.optionsDict[k] is not None
]
def readsol(self, filename):
"""Read a CPLEX solution file"""
# CPLEX solution codes: http://www-eio.upc.es/lceio/manuals/cplex-11/html/overviewcplex/statuscodes.html
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")
statusValue = solutionheader.get("solutionStatusValue")
cplexStatus = {
"1": constants.LpStatusOptimal, # optimal
"101": constants.LpStatusOptimal, # mip optimal
"102": constants.LpStatusOptimal, # mip optimal tolerance
"104": constants.LpStatusOptimal, # max solution limit
"105": constants.LpStatusOptimal, # node limit feasible
"107": constants.LpStatusOptimal, # time lim feasible
"109": constants.LpStatusOptimal, # fail but feasible
"113": constants.LpStatusOptimal, # abort feasible
}
if statusValue not in cplexStatus:
raise PulpSolverError(
"Unknown status returned by CPLEX: \ncode: '{}', string: '{}'".format(
statusValue, statusString
)
)
status = cplexStatus[statusValue]
# we check for integer feasible status to differentiate from optimal in solution status
cplexSolStatus = {
"104": constants.LpSolutionIntegerFeasible, # max solution limit
"105": constants.LpSolutionIntegerFeasible, # node limit feasible
"107": constants.LpSolutionIntegerFeasible, # time lim feasible
"109": constants.LpSolutionIntegerFeasible, # fail but feasible
"111": constants.LpSolutionIntegerFeasible, # memory limit feasible
"113": constants.LpSolutionIntegerFeasible, # abort feasible
}
solStatus = cplexSolStatus.get(statusValue)
shadowPrices = {}
slacks = {}
constraints = solutionXML.find("linearConstraints")
for constraint in constraints:
name = constraint.get("name")
slack = constraint.get("slack")
shadowPrice = constraint.get("dual")
try:
# See issue #508
shadowPrices[name] = float(shadowPrice)
except TypeError:
shadowPrices[name] = None
slacks[name] = float(slack)
values = {}
reducedCosts = {}
for variable in solutionXML.find("variables"):
name = variable.get("name")
value = v | (mip=True, msg=True, timeLimit=None, gapRel=None, gapAbs=None, options=None, warmStart=False, keepFiles=False, path=None, threads=None, logPath=None, maxMemory=None, maxNodes=None) | [
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|
38,117 | pulp.apis.cplex_api | __init__ |
: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 float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param float gapAbs: absolute gap tolerance for the solver to stop
:param int threads: sets the maximum number of threads
:param list options: list of additional options to pass to solver
:param bool warmStart: if True, the solver will use the current value of variables as a start
: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
:param str logPath: path to the log file
:param float maxMemory: max memory to use during the solving. Stops the solving when reached.
:param int maxNodes: max number of nodes during branching. Stops the solving when reached.
| def __init__(
self,
mip=True,
msg=True,
timeLimit=None,
gapRel=None,
gapAbs=None,
options=None,
warmStart=False,
keepFiles=False,
path=None,
threads=None,
logPath=None,
maxMemory=None,
maxNodes=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 float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param float gapAbs: absolute gap tolerance for the solver to stop
:param int threads: sets the maximum number of threads
:param list options: list of additional options to pass to solver
:param bool warmStart: if True, the solver will use the current value of variables as a start
: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
:param str logPath: path to the log file
:param float maxMemory: max memory to use during the solving. Stops the solving when reached.
:param int maxNodes: max number of nodes during branching. Stops the solving when reached.
"""
LpSolver_CMD.__init__(
self,
gapRel=gapRel,
mip=mip,
msg=msg,
timeLimit=timeLimit,
options=options,
maxMemory=maxMemory,
maxNodes=maxNodes,
warmStart=warmStart,
path=path,
keepFiles=keepFiles,
threads=threads,
gapAbs=gapAbs,
logPath=logPath,
)
| (self, mip=True, msg=True, timeLimit=None, gapRel=None, gapAbs=None, options=None, warmStart=False, keepFiles=False, path=None, threads=None, logPath=None, maxMemory=None, maxNodes=None) | [
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|
38,119 | pulp.apis.cplex_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
if not self.executable(self.path):
raise PulpSolverError("PuLP: cannot execute " + self.path)
tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, "lp", "sol", "mst")
vs = 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.optionsDict.get("warmStart", False):
self.writesol(filename=tmpMst, vs=vs)
cplex_cmds += "read " + tmpMst + "\n"
cplex_cmds += "set advance 1\n"
if self.timeLimit is not None:
cplex_cmds += "set timelimit " + str(self.timeLimit) + "\n"
options = self.options + self.getOptions()
for option in 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 os.path.exists(tmpSol):
status = constants.LpStatusInfeasible
values = reducedCosts = shadowPrices = slacks = solStatus = None
else:
(
status,
values,
reducedCosts,
shadowPrices,
slacks,
solStatus,
) = self.readsol(tmpSol)
self.delete_tmp_files(tmpLp, tmpMst, tmpSol)
if self.optionsDict.get("logPath") != "cplex.log":
self.delete_tmp_files("cplex.log")
if status != constants.LpStatusInfeasible:
lp.assignVarsVals(values)
lp.assignVarsDj(reducedCosts)
lp.assignConsPi(shadowPrices)
lp.assignConsSlack(slacks)
lp.assignStatus(status, solStatus)
return status
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|
38,123 | pulp.apis.cplex_api | defaultPath | null | def defaultPath(self):
return self.executableExtension("cplex")
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|
38,128 | pulp.apis.cplex_api | getOptions | null | def getOptions(self):
# CPLEX parameters: https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.6.0/ilog.odms.cplex.help/CPLEX/GettingStarted/topics/tutorials/InteractiveOptimizer/settingParams.html
# CPLEX status: https://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.10.0/ilog.odms.cplex.help/refcallablelibrary/macros/Solution_status_codes.html
params_eq = dict(
logPath="set logFile {}",
gapRel="set mip tolerances mipgap {}",
gapAbs="set mip tolerances absmipgap {}",
maxMemory="set mip limits treememory {}",
threads="set threads {}",
maxNodes="set mip limits nodes {}",
)
return [
v.format(self.optionsDict[k])
for k, v in params_eq.items()
if k in self.optionsDict and self.optionsDict[k] is not None
]
| (self) | [
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|
38,129 | pulp.apis.cplex_api | readsol | Read a CPLEX solution file | def readsol(self, filename):
"""Read a CPLEX solution file"""
# CPLEX solution codes: http://www-eio.upc.es/lceio/manuals/cplex-11/html/overviewcplex/statuscodes.html
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")
statusValue = solutionheader.get("solutionStatusValue")
cplexStatus = {
"1": constants.LpStatusOptimal, # optimal
"101": constants.LpStatusOptimal, # mip optimal
"102": constants.LpStatusOptimal, # mip optimal tolerance
"104": constants.LpStatusOptimal, # max solution limit
"105": constants.LpStatusOptimal, # node limit feasible
"107": constants.LpStatusOptimal, # time lim feasible
"109": constants.LpStatusOptimal, # fail but feasible
"113": constants.LpStatusOptimal, # abort feasible
}
if statusValue not in cplexStatus:
raise PulpSolverError(
"Unknown status returned by CPLEX: \ncode: '{}', string: '{}'".format(
statusValue, statusString
)
)
status = cplexStatus[statusValue]
# we check for integer feasible status to differentiate from optimal in solution status
cplexSolStatus = {
"104": constants.LpSolutionIntegerFeasible, # max solution limit
"105": constants.LpSolutionIntegerFeasible, # node limit feasible
"107": constants.LpSolutionIntegerFeasible, # time lim feasible
"109": constants.LpSolutionIntegerFeasible, # fail but feasible
"111": constants.LpSolutionIntegerFeasible, # memory limit feasible
"113": constants.LpSolutionIntegerFeasible, # abort feasible
}
solStatus = cplexSolStatus.get(statusValue)
shadowPrices = {}
slacks = {}
constraints = solutionXML.find("linearConstraints")
for constraint in constraints:
name = constraint.get("name")
slack = constraint.get("slack")
shadowPrice = constraint.get("dual")
try:
# See issue #508
shadowPrices[name] = float(shadowPrice)
except TypeError:
shadowPrices[name] = None
slacks[name] = float(slack)
values = {}
reducedCosts = {}
for variable in solutionXML.find("variables"):
name = variable.get("name")
value = variable.get("value")
values[name] = float(value)
reducedCost = variable.get("reducedCost")
try:
# See issue #508
reducedCosts[name] = float(reducedCost)
except TypeError:
reducedCosts[name] = None
return status, values, reducedCosts, shadowPrices, slacks, solStatus
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|
38,137 | pulp.apis.cplex_api | writesol | Writes a CPLEX solution file | def writesol(self, filename, vs):
"""Writes a CPLEX solution file"""
try:
import xml.etree.ElementTree as et
except ImportError:
import elementtree.ElementTree as et
root = et.Element("CPLEXSolution", version="1.2")
attrib_head = dict()
attrib_quality = dict()
et.SubElement(root, "header", attrib=attrib_head)
et.SubElement(root, "header", attrib=attrib_quality)
variables = et.SubElement(root, "variables")
values = [(v.name, v.value()) for v in vs if v.value() is not None]
for index, (name, value) in enumerate(values):
attrib_vars = dict(name=name, value=str(value), index=str(index))
et.SubElement(variables, "variable", attrib=attrib_vars)
mst = et.ElementTree(root)
mst.write(filename, encoding="utf-8", xml_declaration=True)
return True
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|
38,160 | pulp.apis.cplex_api | CPLEX_PY |
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
| 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
"""
name = "CPLEX_PY"
try:
global cplex
import cplex
except Exception as e:
err = e
"""The CPLEX LP/MIP solver from python. 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(f"CPLEX_PY: Not Available:\n{self.err}")
else:
def __init__(
self,
mip=True,
msg=True,
timeLimit=None,
gapRel=None,
warmStart=False,
logPath=None,
threads=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 float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param bool warmStart: if True, the solver will use the current value of variables as a start
:param str logPath: path to the log file
:param int threads: number of threads to be used by CPLEX to solve a problem (default None uses all available)
"""
LpSolver.__init__(
self,
gapRel=gapRel,
mip=mip,
msg=msg,
timeLimit=timeLimit,
warmStart=warmStart,
logPath=logPath,
threads=threads,
)
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
"""
model_variables = lp.variables()
self.n2v = {var.name: var for var in model_variables}
if len(self.n2v) != len(model_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 == constants.LpMaximize:
lp.solverModel.objective.set_sense(
lp.solverModel.objective.sense.maximize
)
obj = [float(lp.objective.get(var, 0.0)) for var in model_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 model_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 model_variables]
colnames = [var.name for var in model_variables]
def cplex_var_types(var):
if var.cat == constants.LpInteger:
return "I"
else:
return "C"
ctype = [cplex_var_types(var) for var in model_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 == constants.LpConstraintLE:
senses.append("L")
elif constraint.sense == constants.LpConstraintGE:
senses.append("G")
elif constraint.sense == constants.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.setlogfile(None)
logPath = self.optionsDict.get("logPath")
if logPath is not None:
if self.msg:
warnings.warn(
"`logPath` argument replaces `msg=1`. The output will be redirected to the log file."
)
self.setlogfile(open(logPath, "w"))
gapRel = self.optionsDict.get("gapRel")
if gapRel is not None:
self.changeEpgap(gapRel)
if self.timeLimit is not None:
self.setTimeLimit(self.timeLimit)
self.setThreads(self.optionsDict.get("threads", None))
if self.optionsDict.get("warmStart", False):
# We assume "auto" for the effort_level
effort = self.solverModel.MIP_starts.effort_level.auto
start = [
(k, v.value()) for k, v in self.n2v.items() if v.value() is not None
]
if not start:
warnings.warn("No variable with value found: mipStart aborted")
return
ind, val = zip(*start)
self.solverModel.MIP_starts.add(
cplex.SparsePair(ind=ind, val=val), effort, "1"
)
def setlogfile(self, fileobj):
"""
sets the logfile for cplex output
"""
self.solverModel.set_error_stream(fileobj)
self.solverModel.set_log_stream(fileobj)
self.solverModel.set_warning_stream(fileobj)
self.solverModel.set_results_stream(fileobj)
def setThreads(self, threads=None):
"""
Change cplex thread count used (None is default which uses all available resources)
"""
self.solverModel.parameters.threads.set(threads or 0)
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):
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|
38,163 | pulp.apis.cplex_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
raise PulpSolverError(f"CPLEX_PY: Not Available:\n{self.err}")
| (self, lp) | [
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|
38,201 | pulp.apis.scip_api | FSCIP_CMD | The multi-threaded FiberSCIP version of the SCIP optimization solver | class FSCIP_CMD(LpSolver_CMD):
"""The multi-threaded FiberSCIP version of the SCIP optimization solver"""
name = "FSCIP_CMD"
def __init__(
self,
path=None,
mip=True,
keepFiles=False,
msg=True,
options=None,
timeLimit=None,
gapRel=None,
gapAbs=None,
maxNodes=None,
threads=None,
logPath=None,
):
"""
:param bool msg: if False, no log is shown
:param bool mip: if False, assume LP even if integer variables
: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
:param float timeLimit: maximum time for solver (in seconds)
:param float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param float gapAbs: absolute gap tolerance for the solver to stop
:param int maxNodes: max number of nodes during branching. Stops the solving when reached.
:param int threads: sets the maximum number of threads
:param str logPath: path to the log file
"""
LpSolver_CMD.__init__(
self,
mip=mip,
msg=msg,
options=options,
path=path,
keepFiles=keepFiles,
timeLimit=timeLimit,
gapRel=gapRel,
gapAbs=gapAbs,
maxNodes=maxNodes,
threads=threads,
logPath=logPath,
)
FSCIP_STATUSES = {
"No Solution": constants.LpStatusNotSolved,
"Final Solution": constants.LpStatusOptimal,
}
NO_SOLUTION_STATUSES = {
constants.LpStatusInfeasible,
constants.LpStatusUnbounded,
constants.LpStatusNotSolved,
}
def defaultPath(self):
return self.executableExtension(fscip_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)
tmpLp, tmpSol, tmpOptions, tmpParams = self.create_tmp_files(
lp.name, "lp", "sol", "set", "prm"
)
lp.writeLP(tmpLp)
file_options: List[str] = []
if self.timeLimit is not None:
file_options.append(f"limits/time={self.timeLimit}")
if "gapRel" in self.optionsDict:
file_options.append(f"limits/gap={self.optionsDict['gapRel']}")
if "gapAbs" in self.optionsDict:
file_options.append(f"limits/absgap={self.optionsDict['gapAbs']}")
if "maxNodes" in self.optionsDict:
file_options.append(f"limits/nodes={self.optionsDict['maxNodes']}")
if not self.mip:
warnings.warn(f"{self.name} does not allow a problem to be relaxed")
file_parameters: List[str] = []
# disable presolving in the LoadCoordinator to make sure a solution file is always written
file_parameters.append("NoPreprocessingInLC = TRUE")
command: List[str] = []
command.append(self.path)
command.append(tmpParams)
command.append(tmpLp)
command.extend(["-s", tmpOptions])
command.extend(["-fsol", tmpSol])
if not self.msg:
command.append("-q")
if "logPath" in self.optionsDict:
command.extend(["-l", self.optionsDict["logPath"]])
if "threads" in self.optionsDict:
command.extend(["-sth", f"{self.optionsDict['threads']}"])
options = iter(self.options)
for option in options:
# identify cli options by a leading dash (-) and treat other options as file options
if option.startswith("-"):
# assumption: all cli options require an argument which is provided as a separate parameter
argument = next(options)
command.extend([option, argument])
else:
# assumption: all file options contain a slash (/)
is_file_options = "/" in option
# assumption: all file options and parameters require an argument which is provided after the equal sign (=)
if "=" not in option:
argument = next(options)
option += f"={argument}"
if is_file_options:
file_options.append(option)
else:
file_parameters.append(option)
# wipe the solution file since FSCIP does not overwrite it if no solution was found which causes parsing errors
self.silent_remove(tmpSol)
with open(tmpOptions, "w") as options_file:
options_file.write("\n".join(file_options))
with open(tmpParams, "w") as parameters_file:
parameters_file.write("\n".join(file_parameters))
subprocess.check_call(
command,
stdout=sys.stdout if self.msg else subprocess.DEVNULL,
stderr=sys.stderr if self.msg else subprocess.DEVNULL,
)
if not os.path.exists(tmpSol):
raise PulpSolverError("PuLP: Error while executing " + self.path)
status, values = self.readsol(tmpSol)
# Make sure to add back in any 0-valued variables SCIP leaves out.
finalVals = {}
for v in lp.variables():
finalVals[v.name] = values.get(v.name, 0.0)
lp.assignVarsVals(finalVals)
lp.assignStatus(status)
self.delete_tmp_files(tmpLp, tmpSol, tmpOptions, tmpParams)
return status
@staticmethod
def parse_status(string: str) -> Optional[int]:
for fscip_status, pulp_status in FSCIP_CMD.FSCIP_STATUSES.items():
if fscip_status in string:
return pulp_status
return None
@staticmethod
def parse_objective(string: str) -> Optional[float]:
fields = string.split(":")
if len(fields) != 2:
return None
label, objective = fields
if label != "objective value":
return None
objective = objective.strip()
try:
objective = float(objective)
except ValueError:
return None
return objective
@staticmethod
def parse_variable(string: str) -> Optional[Tuple[str, float]]:
fields = string.split()
if len(fields) < 2:
return None
name, value = fields[:2]
try:
value = float(value)
except ValueError:
return None
return name, value
@staticmethod
def readsol(filename):
"""Read a FSCIP solution file"""
with open(filename) as file:
# First line must contain a solution status
status_line = file.readline()
status = FSCIP_CMD.parse_status(status_line)
if status is None:
raise PulpSolverError(f"Can't get FSCIP solver status: {status_line!r}")
if status in FSCIP_CMD.NO_SOLUTION_STATUSES:
return status, {}
# Look for an objective value. If we can't find one, stop.
objective_line = file.readline()
objective = FSCIP_CMD.parse_objective(objective_line)
if objective is None:
raise PulpSolverError(
f"Can't get FSCIP solver objective: {objective_line!r}"
)
# Parse the variable values.
variables: Dict[str, float] = {}
for variable_line in file:
variable = FSCIP_CMD.parse_variable(variable_line)
if variable is None:
raise PulpSolverError(
f"Can't read FSCIP solver output: {variable_line!r}"
)
name, value = variable
variables[name] = value
return status, variables
| (path=None, mip=True, keepFiles=False, msg=True, options=None, timeLimit=None, gapRel=None, gapAbs=None, maxNodes=None, threads=None, logPath=None) | [
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|
38,202 | pulp.apis.scip_api | __init__ |
:param bool msg: if False, no log is shown
:param bool mip: if False, assume LP even if integer variables
: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
:param float timeLimit: maximum time for solver (in seconds)
:param float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param float gapAbs: absolute gap tolerance for the solver to stop
:param int maxNodes: max number of nodes during branching. Stops the solving when reached.
:param int threads: sets the maximum number of threads
:param str logPath: path to the log file
| def __init__(
self,
path=None,
mip=True,
keepFiles=False,
msg=True,
options=None,
timeLimit=None,
gapRel=None,
gapAbs=None,
maxNodes=None,
threads=None,
logPath=None,
):
"""
:param bool msg: if False, no log is shown
:param bool mip: if False, assume LP even if integer variables
: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
:param float timeLimit: maximum time for solver (in seconds)
:param float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param float gapAbs: absolute gap tolerance for the solver to stop
:param int maxNodes: max number of nodes during branching. Stops the solving when reached.
:param int threads: sets the maximum number of threads
:param str logPath: path to the log file
"""
LpSolver_CMD.__init__(
self,
mip=mip,
msg=msg,
options=options,
path=path,
keepFiles=keepFiles,
timeLimit=timeLimit,
gapRel=gapRel,
gapAbs=gapAbs,
maxNodes=maxNodes,
threads=threads,
logPath=logPath,
)
| (self, path=None, mip=True, keepFiles=False, msg=True, options=None, timeLimit=None, gapRel=None, gapAbs=None, maxNodes=None, threads=None, logPath=None) | [
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|
38,204 | pulp.apis.scip_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
if not self.executable(self.path):
raise PulpSolverError("PuLP: cannot execute " + self.path)
tmpLp, tmpSol, tmpOptions, tmpParams = self.create_tmp_files(
lp.name, "lp", "sol", "set", "prm"
)
lp.writeLP(tmpLp)
file_options: List[str] = []
if self.timeLimit is not None:
file_options.append(f"limits/time={self.timeLimit}")
if "gapRel" in self.optionsDict:
file_options.append(f"limits/gap={self.optionsDict['gapRel']}")
if "gapAbs" in self.optionsDict:
file_options.append(f"limits/absgap={self.optionsDict['gapAbs']}")
if "maxNodes" in self.optionsDict:
file_options.append(f"limits/nodes={self.optionsDict['maxNodes']}")
if not self.mip:
warnings.warn(f"{self.name} does not allow a problem to be relaxed")
file_parameters: List[str] = []
# disable presolving in the LoadCoordinator to make sure a solution file is always written
file_parameters.append("NoPreprocessingInLC = TRUE")
command: List[str] = []
command.append(self.path)
command.append(tmpParams)
command.append(tmpLp)
command.extend(["-s", tmpOptions])
command.extend(["-fsol", tmpSol])
if not self.msg:
command.append("-q")
if "logPath" in self.optionsDict:
command.extend(["-l", self.optionsDict["logPath"]])
if "threads" in self.optionsDict:
command.extend(["-sth", f"{self.optionsDict['threads']}"])
options = iter(self.options)
for option in options:
# identify cli options by a leading dash (-) and treat other options as file options
if option.startswith("-"):
# assumption: all cli options require an argument which is provided as a separate parameter
argument = next(options)
command.extend([option, argument])
else:
# assumption: all file options contain a slash (/)
is_file_options = "/" in option
# assumption: all file options and parameters require an argument which is provided after the equal sign (=)
if "=" not in option:
argument = next(options)
option += f"={argument}"
if is_file_options:
file_options.append(option)
else:
file_parameters.append(option)
# wipe the solution file since FSCIP does not overwrite it if no solution was found which causes parsing errors
self.silent_remove(tmpSol)
with open(tmpOptions, "w") as options_file:
options_file.write("\n".join(file_options))
with open(tmpParams, "w") as parameters_file:
parameters_file.write("\n".join(file_parameters))
subprocess.check_call(
command,
stdout=sys.stdout if self.msg else subprocess.DEVNULL,
stderr=sys.stderr if self.msg else subprocess.DEVNULL,
)
if not os.path.exists(tmpSol):
raise PulpSolverError("PuLP: Error while executing " + self.path)
status, values = self.readsol(tmpSol)
# Make sure to add back in any 0-valued variables SCIP leaves out.
finalVals = {}
for v in lp.variables():
finalVals[v.name] = values.get(v.name, 0.0)
lp.assignVarsVals(finalVals)
lp.assignStatus(status)
self.delete_tmp_files(tmpLp, tmpSol, tmpOptions, tmpParams)
return status
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|
38,208 | pulp.apis.scip_api | defaultPath | null | def defaultPath(self):
return self.executableExtension(fscip_path)
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|
38,213 | pulp.apis.scip_api | parse_objective | null | @staticmethod
def parse_objective(string: str) -> Optional[float]:
fields = string.split(":")
if len(fields) != 2:
return None
label, objective = fields
if label != "objective value":
return None
objective = objective.strip()
try:
objective = float(objective)
except ValueError:
return None
return objective
| (string: str) -> Optional[float] | [
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|
38,214 | pulp.apis.scip_api | parse_status | null | @staticmethod
def parse_status(string: str) -> Optional[int]:
for fscip_status, pulp_status in FSCIP_CMD.FSCIP_STATUSES.items():
if fscip_status in string:
return pulp_status
return None
| (string: str) -> Optional[int] | [
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|
38,215 | pulp.apis.scip_api | parse_variable | null | @staticmethod
def parse_variable(string: str) -> Optional[Tuple[str, float]]:
fields = string.split()
if len(fields) < 2:
return None
name, value = fields[:2]
try:
value = float(value)
except ValueError:
return None
return name, value
| (string: str) -> Optional[Tuple[str, float]] | [
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|
38,216 | pulp.apis.scip_api | readsol | Read a FSCIP solution file | @staticmethod
def readsol(filename):
"""Read a FSCIP solution file"""
with open(filename) as file:
# First line must contain a solution status
status_line = file.readline()
status = FSCIP_CMD.parse_status(status_line)
if status is None:
raise PulpSolverError(f"Can't get FSCIP solver status: {status_line!r}")
if status in FSCIP_CMD.NO_SOLUTION_STATUSES:
return status, {}
# Look for an objective value. If we can't find one, stop.
objective_line = file.readline()
objective = FSCIP_CMD.parse_objective(objective_line)
if objective is None:
raise PulpSolverError(
f"Can't get FSCIP solver objective: {objective_line!r}"
)
# Parse the variable values.
variables: Dict[str, float] = {}
for variable_line in file:
variable = FSCIP_CMD.parse_variable(variable_line)
if variable is None:
raise PulpSolverError(
f"Can't read FSCIP solver output: {variable_line!r}"
)
name, value = variable
variables[name] = value
return status, variables
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|
38,247 | pulp.pulp | FixedElasticSubProblem |
Contains the subproblem generated by converting a fixed constraint
:math:`\sum_{i}a_i x_i = b` into an elastic constraint.
:param constraint: The LpConstraint that the elastic constraint is based on
:param penalty: penalty applied for violation (+ve or -ve) of the constraints
:param proportionFreeBound:
the proportional bound (+ve and -ve) on
constraint violation that is free from penalty
:param proportionFreeBoundList: the proportional bound on constraint violation that is free from penalty, expressed as a list where [-ve, +ve]
| class FixedElasticSubProblem(LpProblem):
"""
Contains the subproblem generated by converting a fixed constraint
:math:`\\sum_{i}a_i x_i = b` into an elastic constraint.
:param constraint: The LpConstraint that the elastic constraint is based on
:param penalty: penalty applied for violation (+ve or -ve) of the constraints
:param proportionFreeBound:
the proportional bound (+ve and -ve) on
constraint violation that is free from penalty
:param proportionFreeBoundList: the proportional bound on \
constraint violation that is free from penalty, expressed as a list\
where [-ve, +ve]
"""
def __init__(
self,
constraint,
penalty=None,
proportionFreeBound=None,
proportionFreeBoundList=None,
):
subProblemName = f"{constraint.name}_elastic_SubProblem"
LpProblem.__init__(self, subProblemName, const.LpMinimize)
self.objective = LpAffineExpression()
self.constraint = constraint
self.constant = constraint.constant
self.RHS = -constraint.constant
self.objective = LpAffineExpression()
self += constraint, "_Constraint"
# create and add these variables but disabled
self.freeVar = LpVariable("_free_bound", upBound=0, lowBound=0)
self.upVar = LpVariable("_pos_penalty_var", upBound=0, lowBound=0)
self.lowVar = LpVariable("_neg_penalty_var", upBound=0, lowBound=0)
constraint.addInPlace(self.freeVar + self.lowVar + self.upVar)
if proportionFreeBound:
proportionFreeBoundList = [proportionFreeBound, proportionFreeBound]
if proportionFreeBoundList:
# add a costless variable
self.freeVar.upBound = abs(constraint.constant * proportionFreeBoundList[0])
self.freeVar.lowBound = -abs(
constraint.constant * proportionFreeBoundList[1]
)
# Note the reversal of the upbound and lowbound due to the nature of the
# variable
if penalty is not None:
# activate these variables
self.upVar.upBound = None
self.lowVar.lowBound = None
self.objective = penalty * self.upVar - penalty * self.lowVar
def _findValue(self, attrib):
"""
safe way to get the value of a variable that may not exist
"""
var = getattr(self, attrib, 0)
if var:
if value(var) is not None:
return value(var)
else:
return 0.0
else:
return 0.0
def isViolated(self):
"""
returns true if the penalty variables are non-zero
"""
upVar = self._findValue("upVar")
lowVar = self._findValue("lowVar")
freeVar = self._findValue("freeVar")
result = abs(upVar + lowVar) >= const.EPS
if result:
log.debug(
"isViolated %s, upVar %s, lowVar %s, freeVar %s result %s"
% (self.name, upVar, lowVar, freeVar, result)
)
log.debug(f"isViolated value lhs {self.findLHSValue()} constant {self.RHS}")
return result
def findDifferenceFromRHS(self):
"""
The amount the actual value varies from the RHS (sense: LHS - RHS)
"""
return self.findLHSValue() - self.RHS
def findLHSValue(self):
"""
for elastic constraints finds the LHS value of the constraint without
the free variable and or penalty variable assumes the constant is on the
rhs
"""
upVar = self._findValue("upVar")
lowVar = self._findValue("lowVar")
freeVar = self._findValue("freeVar")
return self.constraint.value() - self.constant - upVar - lowVar - freeVar
def deElasticize(self):
"""de-elasticize constraint"""
self.upVar.upBound = 0
self.lowVar.lowBound = 0
def reElasticize(self):
"""
Make the Subproblem elastic again after deElasticize
"""
self.upVar.lowBound = 0
self.upVar.upBound = None
self.lowVar.upBound = 0
self.lowVar.lowBound = None
def alterName(self, name):
"""
Alters the name of anonymous parts of the problem
"""
self.name = f"{name}_elastic_SubProblem"
if hasattr(self, "freeVar"):
self.freeVar.name = self.name + "_free_bound"
if hasattr(self, "upVar"):
self.upVar.name = self.name + "_pos_penalty_var"
if hasattr(self, "lowVar"):
self.lowVar.name = self.name + "_neg_penalty_var"
| (constraint, penalty=None, proportionFreeBound=None, proportionFreeBoundList=None) | [
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|
38,248 | pulp.pulp | __getstate__ | null | def __getstate__(self):
# Remove transient data prior to pickling.
state = self.__dict__.copy()
del state["_variable_ids"]
return state
| (self) | [
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|
38,249 | pulp.pulp | __iadd__ | null | def __iadd__(self, other):
if isinstance(other, tuple):
other, name = other
else:
name = None
if other is True:
return self
elif other is False:
raise TypeError("A False object cannot be passed as a constraint")
elif isinstance(other, LpConstraintVar):
self.addConstraint(other.constraint)
elif isinstance(other, LpConstraint):
self.addConstraint(other, name)
elif isinstance(other, LpAffineExpression):
if self.objective is not None:
warnings.warn("Overwriting previously set objective.")
self.objective = other
if name is not None:
# we may keep the LpAffineExpression name
self.objective.name = name
elif isinstance(other, LpVariable) or isinstance(other, (int, float)):
if self.objective is not None:
warnings.warn("Overwriting previously set objective.")
self.objective = LpAffineExpression(other)
self.objective.name = name
else:
raise TypeError(
"Can only add LpConstraintVar, LpConstraint, LpAffineExpression or True objects"
)
return self
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|
38,250 | pulp.pulp | __init__ | null | def __init__(
self,
constraint,
penalty=None,
proportionFreeBound=None,
proportionFreeBoundList=None,
):
subProblemName = f"{constraint.name}_elastic_SubProblem"
LpProblem.__init__(self, subProblemName, const.LpMinimize)
self.objective = LpAffineExpression()
self.constraint = constraint
self.constant = constraint.constant
self.RHS = -constraint.constant
self.objective = LpAffineExpression()
self += constraint, "_Constraint"
# create and add these variables but disabled
self.freeVar = LpVariable("_free_bound", upBound=0, lowBound=0)
self.upVar = LpVariable("_pos_penalty_var", upBound=0, lowBound=0)
self.lowVar = LpVariable("_neg_penalty_var", upBound=0, lowBound=0)
constraint.addInPlace(self.freeVar + self.lowVar + self.upVar)
if proportionFreeBound:
proportionFreeBoundList = [proportionFreeBound, proportionFreeBound]
if proportionFreeBoundList:
# add a costless variable
self.freeVar.upBound = abs(constraint.constant * proportionFreeBoundList[0])
self.freeVar.lowBound = -abs(
constraint.constant * proportionFreeBoundList[1]
)
# Note the reversal of the upbound and lowbound due to the nature of the
# variable
if penalty is not None:
# activate these variables
self.upVar.upBound = None
self.lowVar.lowBound = None
self.objective = penalty * self.upVar - penalty * self.lowVar
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|
38,251 | pulp.pulp | __repr__ | null | def __repr__(self):
s = self.name + ":\n"
if self.sense == 1:
s += "MINIMIZE\n"
else:
s += "MAXIMIZE\n"
s += repr(self.objective) + "\n"
if self.constraints:
s += "SUBJECT TO\n"
for n, c in self.constraints.items():
s += c.asCplexLpConstraint(n) + "\n"
s += "VARIABLES\n"
for v in self.variables():
s += v.asCplexLpVariable() + " " + const.LpCategories[v.cat] + "\n"
return s
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|
38,252 | pulp.pulp | __setstate__ | null | def __setstate__(self, state):
# Update transient data prior to unpickling.
self.__dict__.update(state)
self._variable_ids = {}
for v in self._variables:
self._variable_ids[v.hash] = v
| (self, state) | [
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|
38,253 | pulp.pulp | _findValue |
safe way to get the value of a variable that may not exist
| def _findValue(self, attrib):
"""
safe way to get the value of a variable that may not exist
"""
var = getattr(self, attrib, 0)
if var:
if value(var) is not None:
return value(var)
else:
return 0.0
else:
return 0.0
| (self, attrib) | [
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|
38,254 | pulp.pulp | add | null | def add(self, constraint, name=None):
self.addConstraint(constraint, name)
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|
38,255 | pulp.pulp | addConstraint | null | 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")
# removed as this test fails for empty constraints
# if len(constraint) == 0:
# if not constraint.valid():
# raise ValueError, "Cannot add false constraints"
if name in self.constraints:
if self.noOverlap:
raise const.PulpError("overlapping constraint names: " + name)
else:
print("Warning: overlapping constraint names:", name)
self.constraints[name] = constraint
self.modifiedConstraints.append(constraint)
self.addVariables(list(constraint.keys()))
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|
38,256 | pulp.pulp | addVariable |
Adds a variable to the problem before a constraint is added
@param variable: the variable to be added
| def addVariable(self, variable):
"""
Adds a variable to the problem before a constraint is added
@param variable: the variable to be added
"""
if variable.hash not in self._variable_ids:
self._variables.append(variable)
self._variable_ids[variable.hash] = variable
| (self, variable) | [
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|
38,257 | pulp.pulp | addVariables |
Adds variables to the problem before a constraint is added
@param variables: the variables to be added
| def addVariables(self, variables):
"""
Adds variables to the problem before a constraint is added
@param variables: the variables to be added
"""
for v in variables:
self.addVariable(v)
| (self, variables) | [
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|
38,258 | pulp.pulp | alterName |
Alters the name of anonymous parts of the problem
| def alterName(self, name):
"""
Alters the name of anonymous parts of the problem
"""
self.name = f"{name}_elastic_SubProblem"
if hasattr(self, "freeVar"):
self.freeVar.name = self.name + "_free_bound"
if hasattr(self, "upVar"):
self.upVar.name = self.name + "_pos_penalty_var"
if hasattr(self, "lowVar"):
self.lowVar.name = self.name + "_neg_penalty_var"
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|
38,259 | pulp.pulp | assignConsPi | null | def assignConsPi(self, values):
for name in values:
try:
self.constraints[name].pi = values[name]
except KeyError:
pass
| (self, values) | [
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|
38,260 | pulp.pulp | assignConsSlack | null | def assignConsSlack(self, values, activity=False):
for name in values:
try:
if activity:
# reports the activity not the slack
self.constraints[name].slack = -1 * (
self.constraints[name].constant + float(values[name])
)
else:
self.constraints[name].slack = float(values[name])
except KeyError:
pass
| (self, values, activity=False) | [
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|
38,261 | pulp.pulp | assignStatus |
Sets the status of the model after solving.
:param status: code for the status of the model
:param sol_status: code for the status of the solution
:return:
| def assignStatus(self, status, sol_status=None):
"""
Sets the status of the model after solving.
:param status: code for the status of the model
:param sol_status: code for the status of the solution
:return:
"""
if status not in const.LpStatus:
raise const.PulpError("Invalid status code: " + str(status))
if sol_status is not None and sol_status not in const.LpSolution:
raise const.PulpError("Invalid solution status code: " + str(sol_status))
self.status = status
if sol_status is None:
sol_status = const.LpStatusToSolution.get(
status, const.LpSolutionNoSolutionFound
)
self.sol_status = sol_status
return True
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|
38,262 | pulp.pulp | assignVarsDj | null | def assignVarsDj(self, values):
variables = self.variablesDict()
for name in values:
if name != "__dummy":
variables[name].dj = values[name]
| (self, values) | [
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|
38,263 | pulp.pulp | assignVarsVals | null | def assignVarsVals(self, values):
variables = self.variablesDict()
for name in values:
if name != "__dummy":
variables[name].varValue = values[name]
| (self, values) | [
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|
38,264 | pulp.pulp | checkDuplicateVars |
Checks if there are at least two variables with the same name
:return: 1
:raises `const.PulpError`: if there ar duplicates
| def checkDuplicateVars(self) -> None:
"""
Checks if there are at least two variables with the same name
:return: 1
:raises `const.PulpError`: if there ar duplicates
"""
name_counter = Counter(variable.name for variable in self.variables())
repeated_names = {
(name, count) for name, count in name_counter.items() if count >= 2
}
if repeated_names:
raise const.PulpError(f"Repeated variable names: {repeated_names}")
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]
|
38,265 | pulp.pulp | checkLengthVars |
Checks if variables have names smaller than `max_length`
:param int max_length: max size for variable name
:return:
:raises const.PulpError: if there is at least one variable that has a long name
| def checkLengthVars(self, max_length: int) -> None:
"""
Checks if variables have names smaller than `max_length`
:param int max_length: max size for variable name
:return:
:raises const.PulpError: if there is at least one variable that has a long name
"""
long_names = [
variable.name
for variable in self.variables()
if len(variable.name) > max_length
]
if long_names:
raise const.PulpError(
f"Variable names too long for Lp format: {long_names}"
)
| (self, max_length: int) -> NoneType | [
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|
38,266 | pulp.pulp | coefficients | null | def coefficients(self, translation=None):
coefs = []
if translation == None:
for c in self.constraints:
cst = self.constraints[c]
coefs.extend([(v.name, c, cst[v]) for v in cst])
else:
for c in self.constraints:
ctr = translation[c]
cst = self.constraints[c]
coefs.extend([(translation[v.name], ctr, cst[v]) for v in cst])
return coefs
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|
38,267 | pulp.pulp | copy | Make a copy of self. Expressions are copied by reference | def copy(self):
"""Make a copy of self. Expressions are copied by reference"""
lpcopy = LpProblem(name=self.name, sense=self.sense)
lpcopy.objective = self.objective
lpcopy.constraints = self.constraints.copy()
lpcopy.sos1 = self.sos1.copy()
lpcopy.sos2 = self.sos2.copy()
return lpcopy
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|
38,268 | pulp.pulp | deElasticize | de-elasticize constraint | def deElasticize(self):
"""de-elasticize constraint"""
self.upVar.upBound = 0
self.lowVar.lowBound = 0
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|
38,269 | pulp.pulp | deepcopy | Make a copy of self. Expressions are copied by value | def deepcopy(self):
"""Make a copy of self. Expressions are copied by value"""
lpcopy = LpProblem(name=self.name, sense=self.sense)
if self.objective is not None:
lpcopy.objective = self.objective.copy()
lpcopy.constraints = {}
for k, v in self.constraints.items():
lpcopy.constraints[k] = v.copy()
lpcopy.sos1 = self.sos1.copy()
lpcopy.sos2 = self.sos2.copy()
return lpcopy
| (self) | [
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|
38,270 | pulp.pulp | extend |
extends an LpProblem by adding constraints either from a dictionary
a tuple or another LpProblem object.
@param use_objective: determines whether the objective is imported from
the other problem
For dictionaries the constraints will be named with the keys
For tuples an unique name will be generated
For LpProblems the name of the problem will be added to the constraints
name
| def extend(self, other, use_objective=True):
"""
extends an LpProblem by adding constraints either from a dictionary
a tuple or another LpProblem object.
@param use_objective: determines whether the objective is imported from
the other problem
For dictionaries the constraints will be named with the keys
For tuples an unique name will be generated
For LpProblems the name of the problem will be added to the constraints
name
"""
if isinstance(other, dict):
for name in other:
self.constraints[name] = other[name]
elif isinstance(other, LpProblem):
for v in set(other.variables()).difference(self.variables()):
v.name = other.name + v.name
for name, c in other.constraints.items():
c.name = other.name + name
self.addConstraint(c)
if use_objective:
self.objective += other.objective
else:
for c in other:
if isinstance(c, tuple):
name = c[0]
c = c[1]
else:
name = None
if not name:
name = c.name
if not name:
name = self.unusedConstraintName()
self.constraints[name] = c
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|
38,271 | pulp.pulp | findDifferenceFromRHS |
The amount the actual value varies from the RHS (sense: LHS - RHS)
| def findDifferenceFromRHS(self):
"""
The amount the actual value varies from the RHS (sense: LHS - RHS)
"""
return self.findLHSValue() - self.RHS
| (self) | [
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|
38,272 | pulp.pulp | findLHSValue |
for elastic constraints finds the LHS value of the constraint without
the free variable and or penalty variable assumes the constant is on the
rhs
| def findLHSValue(self):
"""
for elastic constraints finds the LHS value of the constraint without
the free variable and or penalty variable assumes the constant is on the
rhs
"""
upVar = self._findValue("upVar")
lowVar = self._findValue("lowVar")
freeVar = self._findValue("freeVar")
return self.constraint.value() - self.constant - upVar - lowVar - freeVar
| (self) | [
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|
38,273 | pulp.pulp | fixObjective | null | def fixObjective(self):
if self.objective is None:
self.objective = 0
wasNone = 1
else:
wasNone = 0
if not isinstance(self.objective, LpAffineExpression):
self.objective = LpAffineExpression(self.objective)
if self.objective.isNumericalConstant():
dummyVar = self.get_dummyVar()
self.objective += dummyVar
else:
dummyVar = None
return wasNone, dummyVar
| (self) | [
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|
38,274 | pulp.pulp | getSense | null | def getSense(self):
return self.sense
| (self) | [
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|
38,275 | pulp.pulp | get_dummyVar | null | def get_dummyVar(self):
if self.dummyVar is None:
self.dummyVar = LpVariable("__dummy", 0, 0)
return self.dummyVar
| (self) | [
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|
38,276 | pulp.pulp | infeasibilityGap | null | def infeasibilityGap(self, mip=1):
gap = 0
for v in self.variables():
gap = max(abs(v.infeasibilityGap(mip)), gap)
for c in self.constraints.values():
if not c.valid(0):
gap = max(abs(c.value()), gap)
return gap
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|
38,277 | pulp.pulp | isMIP | null | def isMIP(self):
for v in self.variables():
if v.cat == const.LpInteger:
return 1
return 0
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]
|
38,278 | pulp.pulp | isViolated |
returns true if the penalty variables are non-zero
| def isViolated(self):
"""
returns true if the penalty variables are non-zero
"""
upVar = self._findValue("upVar")
lowVar = self._findValue("lowVar")
freeVar = self._findValue("freeVar")
result = abs(upVar + lowVar) >= const.EPS
if result:
log.debug(
"isViolated %s, upVar %s, lowVar %s, freeVar %s result %s"
% (self.name, upVar, lowVar, freeVar, result)
)
log.debug(f"isViolated value lhs {self.findLHSValue()} constant {self.RHS}")
return result
| (self) | [
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|
38,279 | pulp.pulp | normalisedNames | null | def normalisedNames(self):
constraintsNames = {k: "C%07d" % i for i, k in enumerate(self.constraints)}
_variables = self.variables()
variablesNames = {k.name: "X%07d" % i for i, k in enumerate(_variables)}
return constraintsNames, variablesNames, "OBJ"
| (self) | [
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|
38,280 | pulp.pulp | numConstraints |
:return: number of constraints in model
| def numConstraints(self):
"""
:return: number of constraints in model
"""
return len(self.constraints)
| (self) | [
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|
38,281 | pulp.pulp | numVariables |
:return: number of variables in model
| def numVariables(self):
"""
:return: number of variables in model
"""
return len(self._variable_ids)
| (self) | [
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|
38,282 | pulp.pulp | reElasticize |
Make the Subproblem elastic again after deElasticize
| def reElasticize(self):
"""
Make the Subproblem elastic again after deElasticize
"""
self.upVar.lowBound = 0
self.upVar.upBound = None
self.lowVar.upBound = 0
self.lowVar.lowBound = None
| (self) | [
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|
38,283 | pulp.pulp | resolve |
resolves an Problem using the same solver as previously
| def resolve(self, solver=None, **kwargs):
"""
resolves an Problem using the same solver as previously
"""
if not (solver):
solver = self.solver
if self.resolveOK:
return self.solver.actualResolve(self, **kwargs)
else:
return self.solve(solver=solver, **kwargs)
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|
38,284 | pulp.pulp | restoreObjective | null | def restoreObjective(self, wasNone, dummyVar):
if wasNone:
self.objective = None
elif not dummyVar is None:
self.objective -= dummyVar
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|
38,285 | pulp.pulp | roundSolution |
Rounds the lp variables
Inputs:
- none
Side Effects:
- The lp variables are rounded
| def roundSolution(self, epsInt=1e-5, eps=1e-7):
"""
Rounds the lp variables
Inputs:
- none
Side Effects:
- The lp variables are rounded
"""
for v in self.variables():
v.round(epsInt, eps)
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|
38,286 | pulp.pulp | sequentialSolve |
Solve the given Lp problem with several objective functions.
This function sequentially changes the objective of the problem
and then adds the objective function as a constraint
:param objectives: the list of objectives to be used to solve the problem
:param absoluteTols: the list of absolute tolerances to be applied to
the constraints should be +ve for a minimise objective
:param relativeTols: the list of relative tolerances applied to the constraints
:param solver: the specific solver to be used, defaults to the default solver.
| def sequentialSolve(
self, objectives, absoluteTols=None, relativeTols=None, solver=None, debug=False
):
"""
Solve the given Lp problem with several objective functions.
This function sequentially changes the objective of the problem
and then adds the objective function as a constraint
:param objectives: the list of objectives to be used to solve the problem
:param absoluteTols: the list of absolute tolerances to be applied to
the constraints should be +ve for a minimise objective
:param relativeTols: the list of relative tolerances applied to the constraints
:param solver: the specific solver to be used, defaults to the default solver.
"""
# TODO Add a penalty variable to make problems elastic
# TODO add the ability to accept different status values i.e. infeasible etc
if not (solver):
solver = self.solver
if not (solver):
solver = LpSolverDefault
if not (absoluteTols):
absoluteTols = [0] * len(objectives)
if not (relativeTols):
relativeTols = [1] * len(objectives)
# time it
self.startClock()
statuses = []
for i, (obj, absol, rel) in enumerate(
zip(objectives, absoluteTols, relativeTols)
):
self.setObjective(obj)
status = solver.actualSolve(self)
statuses.append(status)
if debug:
self.writeLP(f"{i}Sequence.lp")
if self.sense == const.LpMinimize:
self += obj <= value(obj) * rel + absol, f"Sequence_Objective_{i}"
elif self.sense == const.LpMaximize:
self += obj >= value(obj) * rel + absol, f"Sequence_Objective_{i}"
self.stopClock()
self.solver = solver
return statuses
| (self, objectives, absoluteTols=None, relativeTols=None, solver=None, debug=False) | [
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|
38,287 | pulp.pulp | setObjective |
Sets the input variable as the objective function. Used in Columnwise Modelling
:param obj: the objective function of type :class:`LpConstraintVar`
Side Effects:
- The objective function is set
| def setObjective(self, obj):
"""
Sets the input variable as the objective function. Used in Columnwise Modelling
:param obj: the objective function of type :class:`LpConstraintVar`
Side Effects:
- The objective function is set
"""
if isinstance(obj, LpVariable):
# allows the user to add a LpVariable as an objective
obj = obj + 0.0
try:
obj = obj.constraint
name = obj.name
except AttributeError:
name = None
self.objective = obj
self.objective.name = name
self.resolveOK = False
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|
38,288 | pulp.pulp | setSolver | Sets the Solver for this problem useful if you are using
resolve
| def setSolver(self, solver=LpSolverDefault):
"""Sets the Solver for this problem useful if you are using
resolve
"""
self.solver = solver
| (self, solver=<pulp.apis.coin_api.PULP_CBC_CMD object at 0x7fae47fd56f0>) | [
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|
38,289 | pulp.pulp | solve |
Solve the given Lp problem.
This function changes the problem to make it suitable for solving
then calls the solver.actualSolve() method to find the solution
:param solver: Optional: the specific solver to be used, defaults to the
default solver.
Side Effects:
- The attributes of the problem object are changed in
:meth:`~pulp.solver.LpSolver.actualSolve()` to reflect the Lp solution
| def solve(self, solver=None, **kwargs):
"""
Solve the given Lp problem.
This function changes the problem to make it suitable for solving
then calls the solver.actualSolve() method to find the solution
:param solver: Optional: the specific solver to be used, defaults to the
default solver.
Side Effects:
- The attributes of the problem object are changed in
:meth:`~pulp.solver.LpSolver.actualSolve()` to reflect the Lp solution
"""
if not (solver):
solver = self.solver
if not (solver):
solver = LpSolverDefault
wasNone, dummyVar = self.fixObjective()
# time it
self.startClock()
status = solver.actualSolve(self, **kwargs)
self.stopClock()
self.restoreObjective(wasNone, dummyVar)
self.solver = solver
return status
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|
38,290 | pulp.pulp | startClock | initializes properties with the current time | def startClock(self):
"initializes properties with the current time"
self.solutionCpuTime = -clock()
self.solutionTime = -time()
| (self) | [
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|
38,291 | pulp.pulp | stopClock | updates time wall time and cpu time | def stopClock(self):
"updates time wall time and cpu time"
self.solutionTime += time()
self.solutionCpuTime += clock()
| (self) | [
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|
38,292 | pulp.pulp | toDict |
creates a dictionary from the model with as much data as possible.
It replaces variables by variable names.
So it requires to have unique names for variables.
:return: dictionary with model data
:rtype: dict
| def toDict(self):
"""
creates a dictionary from the model with as much data as possible.
It replaces variables by variable names.
So it requires to have unique names for variables.
:return: dictionary with model data
:rtype: dict
"""
try:
self.checkDuplicateVars()
except const.PulpError:
raise const.PulpError(
"Duplicated names found in variables:\nto export the model, variable names need to be unique"
)
self.fixObjective()
variables = self.variables()
return dict(
objective=dict(
name=self.objective.name, coefficients=self.objective.toDict()
),
constraints=[v.toDict() for v in self.constraints.values()],
variables=[v.toDict() for v in variables],
parameters=dict(
name=self.name,
sense=self.sense,
status=self.status,
sol_status=self.sol_status,
),
sos1=list(self.sos1.values()),
sos2=list(self.sos2.values()),
)
| (self) | [
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|
38,293 | pulp.pulp | toJson |
Creates a json file from the LpProblem information
:param str filename: filename to write json
:param args: additional arguments for json function
:param kwargs: additional keyword arguments for json function
:return: None
| def toJson(self, filename, *args, **kwargs):
"""
Creates a json file from the LpProblem information
:param str filename: filename to write json
:param args: additional arguments for json function
:param kwargs: additional keyword arguments for json function
:return: None
"""
with open(filename, "w") as f:
json.dump(self.toDict(), f, *args, **kwargs)
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|
38,296 | pulp.pulp | unusedConstraintName | null | def unusedConstraintName(self):
self.lastUnused += 1
while 1:
s = "_C%d" % self.lastUnused
if s not in self.constraints:
break
self.lastUnused += 1
return s
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]
|
38,297 | pulp.pulp | valid | null | def valid(self, eps=0):
for v in self.variables():
if not v.valid(eps):
return False
for c in self.constraints.values():
if not c.valid(eps):
return False
else:
return True
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|
38,298 | pulp.pulp | variables |
Returns the problem variables
:return: A list containing the problem variables
:rtype: (list, :py:class:`LpVariable`)
| def variables(self):
"""
Returns the problem variables
:return: A list containing the problem variables
:rtype: (list, :py:class:`LpVariable`)
"""
if self.objective:
self.addVariables(list(self.objective.keys()))
for c in self.constraints.values():
self.addVariables(list(c.keys()))
self._variables.sort(key=lambda v: v.name)
return self._variables
| (self) | [
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|
38,299 | pulp.pulp | variablesDict | null | def variablesDict(self):
variables = {}
if self.objective:
for v in self.objective:
variables[v.name] = v
for c in list(self.constraints.values()):
for v in c:
variables[v.name] = v
return variables
| (self) | [
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|
38,300 | pulp.pulp | writeLP |
Write the given Lp problem to a .lp file.
This function writes the specifications (objective function,
constraints, variables) of the defined Lp problem to a file.
:param str filename: the name of the file to be created.
:return: variables
Side Effects:
- The file is created
| def writeLP(self, filename, writeSOS=1, mip=1, max_length=100):
"""
Write the given Lp problem to a .lp file.
This function writes the specifications (objective function,
constraints, variables) of the defined Lp problem to a file.
:param str filename: the name of the file to be created.
:return: variables
Side Effects:
- The file is created
"""
return mpslp.writeLP(
self, filename=filename, writeSOS=writeSOS, mip=mip, max_length=max_length
)
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|
38,301 | pulp.pulp | writeMPS |
Writes an mps files from the problem information
:param str filename: name of the file to write
:param int mpsSense:
:param bool rename: if True, normalized names are used for variables and constraints
:param mip: variables and variable renames
:return:
Side Effects:
- The file is created
| def writeMPS(
self, filename, mpsSense=0, rename=0, mip=1, with_objsense: bool = False
):
"""
Writes an mps files from the problem information
:param str filename: name of the file to write
:param int mpsSense:
:param bool rename: if True, normalized names are used for variables and constraints
:param mip: variables and variable renames
:return:
Side Effects:
- The file is created
"""
return mpslp.writeMPS(
self,
filename,
mpsSense=mpsSense,
rename=rename,
mip=mip,
with_objsense=with_objsense,
)
| (self, filename, mpsSense=0, rename=0, mip=1, with_objsense: bool = False) | [
0.020028825849294662,
0.020167915150523186,
0.001486514462158084,
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|
38,302 | pulp.pulp | FractionElasticSubProblem |
Contains the subproblem generated by converting a Fraction constraint
numerator/(numerator+complement) = b
into an elastic constraint
:param name: The name of the elastic subproblem
:param penalty: penalty applied for violation (+ve or -ve) of the constraints
:param proportionFreeBound: the proportional bound (+ve and -ve) on
constraint violation that is free from penalty
:param proportionFreeBoundList: the proportional bound on
constraint violation that is free from penalty, expressed as a list
where [-ve, +ve]
| class FractionElasticSubProblem(FixedElasticSubProblem):
"""
Contains the subproblem generated by converting a Fraction constraint
numerator/(numerator+complement) = b
into an elastic constraint
:param name: The name of the elastic subproblem
:param penalty: penalty applied for violation (+ve or -ve) of the constraints
:param proportionFreeBound: the proportional bound (+ve and -ve) on
constraint violation that is free from penalty
:param proportionFreeBoundList: the proportional bound on
constraint violation that is free from penalty, expressed as a list
where [-ve, +ve]
"""
def __init__(
self,
name,
numerator,
RHS,
sense,
complement=None,
denominator=None,
penalty=None,
proportionFreeBound=None,
proportionFreeBoundList=None,
):
subProblemName = f"{name}_elastic_SubProblem"
self.numerator = numerator
if denominator is None and complement is not None:
self.complement = complement
self.denominator = numerator + complement
elif denominator is not None and complement is None:
self.denominator = denominator
self.complement = denominator - numerator
else:
raise const.PulpError(
"only one of denominator and complement must be specified"
)
self.RHS = RHS
self.lowTarget = self.upTarget = None
LpProblem.__init__(self, subProblemName, const.LpMinimize)
self.freeVar = LpVariable("_free_bound", upBound=0, lowBound=0)
self.upVar = LpVariable("_pos_penalty_var", upBound=0, lowBound=0)
self.lowVar = LpVariable("_neg_penalty_var", upBound=0, lowBound=0)
if proportionFreeBound:
proportionFreeBoundList = [proportionFreeBound, proportionFreeBound]
if proportionFreeBoundList:
upProportionFreeBound, lowProportionFreeBound = proportionFreeBoundList
else:
upProportionFreeBound, lowProportionFreeBound = (0, 0)
# create an objective
self += LpAffineExpression()
# There are three cases if the constraint.sense is ==, <=, >=
if sense in [const.LpConstraintEQ, const.LpConstraintLE]:
# create a constraint the sets the upper bound of target
self.upTarget = RHS + upProportionFreeBound
self.upConstraint = LpFractionConstraint(
self.numerator,
self.complement,
const.LpConstraintLE,
self.upTarget,
denominator=self.denominator,
)
if penalty is not None:
self.lowVar.lowBound = None
self.objective += -1 * penalty * self.lowVar
self.upConstraint += self.lowVar
self += self.upConstraint, "_upper_constraint"
if sense in [const.LpConstraintEQ, const.LpConstraintGE]:
# create a constraint the sets the lower bound of target
self.lowTarget = RHS - lowProportionFreeBound
self.lowConstraint = LpFractionConstraint(
self.numerator,
self.complement,
const.LpConstraintGE,
self.lowTarget,
denominator=self.denominator,
)
if penalty is not None:
self.upVar.upBound = None
self.objective += penalty * self.upVar
self.lowConstraint += self.upVar
self += self.lowConstraint, "_lower_constraint"
def findLHSValue(self):
"""
for elastic constraints finds the LHS value of the constraint without
the free variable and or penalty variable assumes the constant is on the
rhs
"""
# uses code from LpFractionConstraint
if abs(value(self.denominator)) >= const.EPS:
return value(self.numerator) / value(self.denominator)
else:
if abs(value(self.numerator)) <= const.EPS:
# zero divided by zero will return 1
return 1.0
else:
raise ZeroDivisionError
def isViolated(self):
"""
returns true if the penalty variables are non-zero
"""
if abs(value(self.denominator)) >= const.EPS:
if self.lowTarget is not None:
if self.lowTarget > self.findLHSValue():
return True
if self.upTarget is not None:
if self.findLHSValue() > self.upTarget:
return True
else:
# if the denominator is zero the constraint is satisfied
return False
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|
38,305 | pulp.pulp | __init__ | null | def __init__(
self,
name,
numerator,
RHS,
sense,
complement=None,
denominator=None,
penalty=None,
proportionFreeBound=None,
proportionFreeBoundList=None,
):
subProblemName = f"{name}_elastic_SubProblem"
self.numerator = numerator
if denominator is None and complement is not None:
self.complement = complement
self.denominator = numerator + complement
elif denominator is not None and complement is None:
self.denominator = denominator
self.complement = denominator - numerator
else:
raise const.PulpError(
"only one of denominator and complement must be specified"
)
self.RHS = RHS
self.lowTarget = self.upTarget = None
LpProblem.__init__(self, subProblemName, const.LpMinimize)
self.freeVar = LpVariable("_free_bound", upBound=0, lowBound=0)
self.upVar = LpVariable("_pos_penalty_var", upBound=0, lowBound=0)
self.lowVar = LpVariable("_neg_penalty_var", upBound=0, lowBound=0)
if proportionFreeBound:
proportionFreeBoundList = [proportionFreeBound, proportionFreeBound]
if proportionFreeBoundList:
upProportionFreeBound, lowProportionFreeBound = proportionFreeBoundList
else:
upProportionFreeBound, lowProportionFreeBound = (0, 0)
# create an objective
self += LpAffineExpression()
# There are three cases if the constraint.sense is ==, <=, >=
if sense in [const.LpConstraintEQ, const.LpConstraintLE]:
# create a constraint the sets the upper bound of target
self.upTarget = RHS + upProportionFreeBound
self.upConstraint = LpFractionConstraint(
self.numerator,
self.complement,
const.LpConstraintLE,
self.upTarget,
denominator=self.denominator,
)
if penalty is not None:
self.lowVar.lowBound = None
self.objective += -1 * penalty * self.lowVar
self.upConstraint += self.lowVar
self += self.upConstraint, "_upper_constraint"
if sense in [const.LpConstraintEQ, const.LpConstraintGE]:
# create a constraint the sets the lower bound of target
self.lowTarget = RHS - lowProportionFreeBound
self.lowConstraint = LpFractionConstraint(
self.numerator,
self.complement,
const.LpConstraintGE,
self.lowTarget,
denominator=self.denominator,
)
if penalty is not None:
self.upVar.upBound = None
self.objective += penalty * self.upVar
self.lowConstraint += self.upVar
self += self.lowConstraint, "_lower_constraint"
| (self, name, numerator, RHS, sense, complement=None, denominator=None, penalty=None, proportionFreeBound=None, proportionFreeBoundList=None) | [
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|
38,327 | pulp.pulp | findLHSValue |
for elastic constraints finds the LHS value of the constraint without
the free variable and or penalty variable assumes the constant is on the
rhs
| def findLHSValue(self):
"""
for elastic constraints finds the LHS value of the constraint without
the free variable and or penalty variable assumes the constant is on the
rhs
"""
# uses code from LpFractionConstraint
if abs(value(self.denominator)) >= const.EPS:
return value(self.numerator) / value(self.denominator)
else:
if abs(value(self.numerator)) <= const.EPS:
# zero divided by zero will return 1
return 1.0
else:
raise ZeroDivisionError
| (self) | [
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|
38,333 | pulp.pulp | isViolated |
returns true if the penalty variables are non-zero
| def isViolated(self):
"""
returns true if the penalty variables are non-zero
"""
if abs(value(self.denominator)) >= const.EPS:
if self.lowTarget is not None:
if self.lowTarget > self.findLHSValue():
return True
if self.upTarget is not None:
if self.findLHSValue() > self.upTarget:
return True
else:
# if the denominator is zero the constraint is satisfied
return False
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|
38,357 | pulp.apis.glpk_api | GLPK_CMD | The GLPK LP solver | class GLPK_CMD(LpSolver_CMD):
"""The GLPK LP solver"""
name = "GLPK_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(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)
tmpLp, tmpSol = self.create_tmp_files(lp.name, "lp", "sol")
lp.writeLP(tmpLp, writeSOS=0)
proc = ["glpsol", "--cpxlp", tmpLp, "-o", tmpSol]
if self.timeLimit:
proc.extend(["--tmlim", str(self.timeLimit)])
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")
if operating_system == "win":
# Prevent flashing windows if used from a GUI application
startupinfo = subprocess.STARTUPINFO()
startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
rc = subprocess.call(
proc, stdout=pipe, stderr=pipe, startupinfo=startupinfo
)
else:
rc = subprocess.call(proc, stdout=pipe, stderr=pipe)
if rc:
raise PulpSolverError(
"PuLP: Error while trying to execute " + self.path
)
pipe.close()
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)
status, values = self.readsol(tmpSol)
lp.assignVarsVals(values)
lp.assignStatus(status)
self.delete_tmp_files(tmpLp, tmpSol)
return 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": constants.LpStatusOptimal,
"INTEGER NON-OPTIMAL": constants.LpStatusOptimal,
"OPTIMAL": constants.LpStatusOptimal,
"INFEASIBLE (FINAL)": constants.LpStatusInfeasible,
"INTEGER UNDEFINED": constants.LpStatusUndefined,
"UNBOUNDED": constants.LpStatusUnbounded,
"UNDEFINED": constants.LpStatusUndefined,
"INTEGER EMPTY": constants.LpStatusInfeasible,
}
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",
"INTEGER EMPTY",
]
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
| (path=None, keepFiles=False, mip=True, msg=True, options=None, timeLimit=None) | [
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|
38,360 | pulp.apis.glpk_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
if not self.executable(self.path):
raise PulpSolverError("PuLP: cannot execute " + self.path)
tmpLp, tmpSol = self.create_tmp_files(lp.name, "lp", "sol")
lp.writeLP(tmpLp, writeSOS=0)
proc = ["glpsol", "--cpxlp", tmpLp, "-o", tmpSol]
if self.timeLimit:
proc.extend(["--tmlim", str(self.timeLimit)])
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")
if operating_system == "win":
# Prevent flashing windows if used from a GUI application
startupinfo = subprocess.STARTUPINFO()
startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
rc = subprocess.call(
proc, stdout=pipe, stderr=pipe, startupinfo=startupinfo
)
else:
rc = subprocess.call(proc, stdout=pipe, stderr=pipe)
if rc:
raise PulpSolverError(
"PuLP: Error while trying to execute " + self.path
)
pipe.close()
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)
status, values = self.readsol(tmpSol)
lp.assignVarsVals(values)
lp.assignStatus(status)
self.delete_tmp_files(tmpLp, tmpSol)
return status
| (self, lp) | [
0.04103638231754303,
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|
38,364 | pulp.apis.glpk_api | defaultPath | null | def defaultPath(self):
return self.executableExtension(glpk_path)
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|
38,369 | pulp.apis.glpk_api | readsol | Read a GLPK solution file | 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": constants.LpStatusOptimal,
"INTEGER NON-OPTIMAL": constants.LpStatusOptimal,
"OPTIMAL": constants.LpStatusOptimal,
"INFEASIBLE (FINAL)": constants.LpStatusInfeasible,
"INTEGER UNDEFINED": constants.LpStatusUndefined,
"UNBOUNDED": constants.LpStatusUnbounded,
"UNDEFINED": constants.LpStatusUndefined,
"INTEGER EMPTY": constants.LpStatusInfeasible,
}
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",
"INTEGER EMPTY",
]
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
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|
38,397 | pulp.apis.gurobi_api | GUROBI |
The Gurobi LP/MIP solver (via its python interface)
The Gurobi variables are available (after a solve) in var.solverVar
Constraints in constraint.solverConstraint
and the Model is in prob.solverModel
| class GUROBI(LpSolver):
"""
The Gurobi LP/MIP solver (via its python interface)
The Gurobi variables are available (after a solve) in var.solverVar
Constraints in constraint.solverConstraint
and the Model is in prob.solverModel
"""
name = "GUROBI"
env = None
try:
sys.path.append(gurobi_path)
# to import the name into the module scope
global gp
import gurobipy as gp
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,
gapRel=None,
warmStart=False,
logPath=None,
env=None,
envOptions=None,
manageEnv=False,
**solverParams,
):
"""
: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 float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param bool warmStart: if True, the solver will use the current value of variables as a start
:param str logPath: path to the log file
:param gp.Env env: Gurobi environment to use. Default None.
:param dict envOptions: environment options.
:param bool manageEnv: if False, assume the environment is handled by the user.
If ``manageEnv`` is set to True, the ``GUROBI`` object creates a
local Gurobi environment and manages all associated Gurobi
resources. Importantly, this enables Gurobi licenses to be freed
and connections terminated when the ``.close()`` function is called
(this function always disposes of the Gurobi model, and the
environment)::
solver = GUROBI(manageEnv=True)
prob.solve(solver)
solver.close() # Must be called to free Gurobi resources.
# All Gurobi models and environments are freed
``manageEnv=True`` is required when setting license or connection
parameters. The ``envOptions`` argument is used to pass parameters
to the Gurobi environment. For example, to connect to a Gurobi
Cluster Manager::
options = {
"CSManager": "<url>",
"CSAPIAccessID": "<access-id>",
"CSAPISecret": "<api-key>",
}
solver = GUROBI(manageEnv=True, envOptions=options)
solver.close()
# Compute server connection terminated
Alternatively, one can also pass a ``gp.Env`` object. In this case,
to be safe, one should still call ``.close()`` to dispose of the
model::
with gp.Env(params=options) as env:
# Pass environment as a parameter
solver = GUROBI(env=env)
prob.solve(solver)
solver.close()
# Still call `close` as this disposes the model which is required to correctly free env
If ``manageEnv`` is set to False (the default), the ``GUROBI``
object uses the global default Gurobi environment which will be
freed once the object is deleted. In this case, one can still call
``.close()`` to dispose of the model::
solver = GUROBI()
prob.solve(solver)
# The global default environment and model remain active
solver.close()
# Only the global default environment remains active
"""
self.env = env
self.env_options = envOptions if envOptions else {}
self.manage_env = False if self.env is not None else manageEnv
self.solver_params = solverParams
self.model = None
self.init_gurobi = False # whether env and model have been initialised
LpSolver.__init__(
self,
mip=mip,
msg=msg,
timeLimit=timeLimit,
gapRel=gapRel,
logPath=logPath,
warmStart=warmStart,
)
# set the output of gurobi
if not self.msg:
if self.manage_env:
self.env_options["OutputFlag"] = 0
else:
self.env_options["OutputFlag"] = 0
self.solver_params["OutputFlag"] = 0
def __del__(self):
self.close()
def close(self):
"""
Must be called when internal Gurobi model and/or environment
requires disposing. The environment (default or otherwise) will be
disposed only if ``manageEnv`` is set to True.
"""
if not self.init_gurobi:
return
self.model.dispose()
if self.manage_env:
self.env.dispose()
def findSolutionValues(self, lp):
model = lp.solverModel
solutionStatus = model.Status
GRB = gp.GRB
# TODO: check status for Integer Feasible
gurobiLpStatus = {
GRB.OPTIMAL: constants.LpStatusOptimal,
GRB.INFEASIBLE: constants.LpStatusInfeasible,
GRB.INF_OR_UNBD: constants.LpStatusInfeasible,
GRB.UNBOUNDED: constants.LpStatusUnbounded,
GRB.ITERATION_LIMIT: constants.LpStatusNotSolved,
GRB.NODE_LIMIT: constants.LpStatusNotSolved,
GRB.TIME_LIMIT: constants.LpStatusNotSolved,
GRB.SOLUTION_LIMIT: constants.LpStatusNotSolved,
GRB.INTERRUPTED: constants.LpStatusNotSolved,
GRB.NUMERIC: constants.LpStatusNotSolved,
}
if self.msg:
print("Gurobi status=", solutionStatus)
lp.resolveOK = True
for var in lp._variables:
var.isModified = False
status = gurobiLpStatus.get(solutionStatus, constants.LpStatusUndefined)
lp.assignStatus(status)
if model.SolCount >= 1:
# populate pulp solution values
for var, value in zip(
lp._variables, model.getAttr(GRB.Attr.X, model.getVars())
):
var.varValue = value
# populate pulp constraints slack
for constr, value in zip(
lp.constraints.values(),
model.getAttr(GRB.Attr.Slack, model.getConstrs()),
):
constr.slack = value
# put pi and slack variables against the constraints
if not model.IsMIP:
for var, value in zip(
lp._variables, model.getAttr(GRB.Attr.RC, model.getVars())
):
var.dj = value
for constr, value in zip(
lp.constraints.values(),
model.getAttr(GRB.Attr.Pi, model.getConstrs()),
):
constr.pi = value
return status
def available(self):
"""True if the solver is available"""
try:
with gp.Env(params=self.env_options):
pass
except gp.GurobiError as e:
warnings.warn(f"GUROBI error: {e}.")
return False
return True
def initGurobi(self):
if self.init_gurobi:
return
else:
self.init_gurobi = True
| (mip=True, msg=True, options=None, timeLimit=None, *args, **kwargs) | [
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|
38,400 | pulp.apis.gurobi_api | actualSolve | Solve a well formulated lp problem | def actualSolve(self, lp, callback=None):
"""Solve a well formulated lp problem"""
raise PulpSolverError("GUROBI: Not Available")
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|
38,409 | pulp.apis.gurobi_api | GUROBI_CMD | The GUROBI_CMD solver | class GUROBI_CMD(LpSolver_CMD):
"""The GUROBI_CMD solver"""
name = "GUROBI_CMD"
def __init__(
self,
mip=True,
msg=True,
timeLimit=None,
gapRel=None,
gapAbs=None,
options=None,
warmStart=False,
keepFiles=False,
path=None,
threads=None,
logPath=None,
mip_start=False,
):
"""
: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 float gapRel: relative gap tolerance for the solver to stop (in fraction)
:param float gapAbs: absolute gap tolerance for the solver to stop
:param int threads: sets the maximum number of threads
:param list options: list of additional options to pass to solver
:param bool warmStart: if True, the solver will use the current value of variables as a start
: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
:param str logPath: path to the log file
"""
LpSolver_CMD.__init__(
self,
gapRel=gapRel,
mip=mip,
msg=msg,
timeLimit=timeLimit,
options=options,
warmStart=warmStart,
path=path,
keepFiles=keepFiles,
threads=threads,
gapAbs=gapAbs,
logPath=logPath,
)
def defaultPath(self):
return self.executableExtension("gurobi_cl")
def available(self):
"""True if the solver is available"""
if not self.executable(self.path):
return False
# we execute gurobi once to check the return code.
# this is to test that the license is active
result = subprocess.Popen(
self.path, stdout=subprocess.PIPE, universal_newlines=True
)
out, err = result.communicate()
if result.returncode == 0:
# normal execution
return True
# error: we display the gurobi message
warnings.warn(f"GUROBI error: {out}.")
return False
def actualSolve(self, lp):
"""Solve a well formulated lp problem"""
if not self.executable(self.path):
raise PulpSolverError("PuLP: cannot execute " + self.path)
tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, "lp", "sol", "mst")
vs = lp.writeLP(tmpLp, writeSOS=1)
try:
os.remove(tmpSol)
except:
pass
cmd = self.path
options = self.options + self.getOptions()
if self.timeLimit is not None:
options.append(("TimeLimit", self.timeLimit))
cmd += " " + " ".join([f"{key}={value}" for key, value in options])
cmd += f" ResultFile={tmpSol}"
if self.optionsDict.get("warmStart", False):
self.writesol(filename=tmpMst, vs=vs)
cmd += f" InputFile={tmpMst}"
if lp.isMIP():
if not self.mip:
warnings.warn("GUROBI_CMD does not allow a problem to be relaxed")
cmd += f" {tmpLp}"
if self.msg:
pipe = None
else:
pipe = open(os.devnull, "w")
return_code = subprocess.call(cmd.split(), stdout=pipe, stderr=pipe)
# Close the pipe now if we used it.
if pipe is not None:
pipe.close()
if return_code != 0:
raise PulpSolverError("PuLP: Error while trying to execute " + self.path)
if not os.path.exists(tmpSol):
# TODO: the status should be infeasible here, I think
status = constants.LpStatusNotSolved
values = reducedCosts = shadowPrices = slacks = None
else:
# TODO: the status should be infeasible here, I think
status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)
self.delete_tmp_files(tmpLp, tmpMst, tmpSol, "gurobi.log")
if status != constants.LpStatusInfeasible:
lp.assignVarsVals(values)
lp.assignVarsDj(reducedCosts)
lp.assignConsPi(shadowPrices)
lp.assignConsSlack(slacks)
lp.assignStatus(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
status = constants.LpStatusNotSolved
return status, {}, {}, {}, {}
# We have no idea what the status is assume optimal
# TODO: check status for Integer Feasible
status = constants.LpStatusOptimal
shadowPrices = {}
slacks = {}
shadowPrices = {}
slacks = {}
values = {}
reducedCosts = {}
for line in my_file:
if line[0] != "#": # skip comments
name, value = line.split()
values[name] = float(value)
return status, values, reducedCosts, shadowPrices, slacks
def writesol(self, filename, vs):
"""Writes a GUROBI solution file"""
values = [(v.name, v.value()) for v in vs if v.value() is not None]
rows = []
for name, value in values:
rows.append(f"{name} {value}")
with open(filename, "w") as f:
f.write("\n".join(rows))
return True
def getOptions(self):
# GUROBI parameters: http://www.gurobi.com/documentation/7.5/refman/parameters.html#sec:Parameters
params_eq = dict(
logPath="LogFile",
gapRel="MIPGap",
gapAbs="MIPGapAbs",
threads="Threads",
)
return [
(v, self.optionsDict[k])
for k, v in params_eq.items()
if k in self.optionsDict and self.optionsDict[k] is not None
]
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|
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