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# PuLP : Python LP Modeler
# Version 1.4.2
# Copyright (c) 2002-2005, Jean-Sebastien Roy (js@jeannot.org)
# Modifications Copyright (c) 2007- Stuart Anthony Mitchell (s.mitchell@auckland.ac.nz)
# $Id:solvers.py 1791 2008-04-23 22:54:34Z smit023 $
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."""
from .core import LpSolver, PulpSolverError
from .. import constants
import sys
class MOSEK(LpSolver):
"""Mosek lp and mip solver (via Mosek Optimizer API)."""
name = "MOSEK"
try:
global mosek
import mosek
env = mosek.Env()
except ImportError:
def available(self):
"""True if Mosek is available."""
return False
def actualSolve(self, lp, callback=None):
"""Solves a well-formulated lp problem."""
raise PulpSolverError("MOSEK : Not Available")
else:
def __init__(
self,
mip=True,
msg=True,
options={},
task_file_name="",
sol_type=mosek.soltype.bas,
):
"""Initializes the Mosek solver.
Keyword arguments:
@param mip: If False, then solve MIP as LP.
@param msg: Enable Mosek log output.
@param options: Accepts a dictionary of Mosek solver parameters. Ignore to
use default parameter values. Eg: options = {mosek.dparam.mio_max_time:30}
sets the maximum time spent by the Mixed Integer optimizer to 30 seconds.
Equivalently, one could also write: options = {"MSK_DPAR_MIO_MAX_TIME":30}
which uses the generic parameter name as used within the solver, instead of
using an object from the Mosek Optimizer API (Python), as before.
@param task_file_name: Writes a Mosek task file of the given name. By default,
no task file will be written. Eg: task_file_name = "eg1.opf".
@param sol_type: Mosek supports three types of solutions: mosek.soltype.bas
(Basic solution, default), mosek.soltype.itr (Interior-point
solution) and mosek.soltype.itg (Integer solution).
For a full list of Mosek parameters (for the Mosek Optimizer API) and supported task file
formats, please see https://docs.mosek.com/9.1/pythonapi/parameters.html#doc-all-parameter-list.
"""
self.mip = mip
self.msg = msg
self.task_file_name = task_file_name
self.solution_type = sol_type
self.options = options
def available(self):
"""True if Mosek is available."""
return True
def setOutStream(self, text):
"""Sets the log-output stream."""
sys.stdout.write(text)
sys.stdout.flush()
def buildSolverModel(self, lp, inf=1e20):
"""Translate the problem into a Mosek task object."""
self.cons = lp.constraints
self.numcons = len(self.cons)
self.cons_dict = {}
i = 0
for c in self.cons:
self.cons_dict[c] = i
i = i + 1
self.vars = list(lp.variables())
self.numvars = len(self.vars)
self.var_dict = {}
# Checking for repeated names
lp.checkDuplicateVars()
self.task = MOSEK.env.Task()
self.task.appendcons(self.numcons)
self.task.appendvars(self.numvars)
if self.msg:
self.task.set_Stream(mosek.streamtype.log, self.setOutStream)
# Adding variables
for i in range(self.numvars):
vname = self.vars[i].name
self.var_dict[vname] = i
self.task.putvarname(i, vname)
# Variable type (Default: Continuous)
if self.mip & (self.vars[i].cat == constants.LpInteger):
self.task.putvartype(i, mosek.variabletype.type_int)
self.solution_type = mosek.soltype.itg
# Variable bounds
vbkey = mosek.boundkey.fr
vup = inf
vlow = -inf
if self.vars[i].lowBound != None:
vlow = self.vars[i].lowBound
if self.vars[i].upBound != None:
vup = self.vars[i].upBound
vbkey = mosek.boundkey.ra
else:
vbkey = mosek.boundkey.lo
elif self.vars[i].upBound != None:
vup = self.vars[i].upBound
vbkey = mosek.boundkey.up
self.task.putvarbound(i, vbkey, vlow, vup)
# Objective coefficient for the current variable.
self.task.putcj(i, lp.objective.get(self.vars[i], 0.0))
# Coefficient matrix
self.A_rows, self.A_cols, self.A_vals = zip(
*[
[self.cons_dict[row], self.var_dict[col], coeff]
for col, row, coeff in lp.coefficients()
]
)
self.task.putaijlist(self.A_rows, self.A_cols, self.A_vals)
# Constraints
self.constraint_data_list = []
for c in self.cons:
cname = self.cons[c].name
if cname != None:
self.task.putconname(self.cons_dict[c], cname)
else:
self.task.putconname(self.cons_dict[c], c)
csense = self.cons[c].sense
cconst = -self.cons[c].constant
clow = -inf
cup = inf
# Constraint bounds
if csense == constants.LpConstraintEQ:
cbkey = mosek.boundkey.fx
clow = cconst
cup = cconst
elif csense == constants.LpConstraintGE:
cbkey = mosek.boundkey.lo
clow = cconst
elif csense == constants.LpConstraintLE:
cbkey = mosek.boundkey.up
cup = cconst
else:
raise PulpSolverError("Invalid constraint type.")
self.constraint_data_list.append([self.cons_dict[c], cbkey, clow, cup])
self.cons_id_list, self.cbkey_list, self.clow_list, self.cup_list = zip(
*self.constraint_data_list
)
self.task.putconboundlist(
self.cons_id_list, self.cbkey_list, self.clow_list, self.cup_list
)
# Objective sense
if lp.sense == constants.LpMaximize:
self.task.putobjsense(mosek.objsense.maximize)
else:
self.task.putobjsense(mosek.objsense.minimize)
def findSolutionValues(self, lp):
"""
Read the solution values and status from the Mosek task object. Note: Since the status
map from mosek.solsta to LpStatus is not exact, it is recommended that one enables the
log output and then refer to Mosek documentation for a better understanding of the
solution (especially in the case of mip problems).
"""
self.solsta = self.task.getsolsta(self.solution_type)
self.solution_status_dict = {
mosek.solsta.optimal: constants.LpStatusOptimal,
mosek.solsta.prim_infeas_cer: constants.LpStatusInfeasible,
mosek.solsta.dual_infeas_cer: constants.LpStatusUnbounded,
mosek.solsta.unknown: constants.LpStatusUndefined,
mosek.solsta.integer_optimal: constants.LpStatusOptimal,
mosek.solsta.prim_illposed_cer: constants.LpStatusNotSolved,
mosek.solsta.dual_illposed_cer: constants.LpStatusNotSolved,
mosek.solsta.prim_feas: constants.LpStatusNotSolved,
mosek.solsta.dual_feas: constants.LpStatusNotSolved,
mosek.solsta.prim_and_dual_feas: constants.LpStatusNotSolved,
}
# Variable values.
try:
self.xx = [0.0] * self.numvars
self.task.getxx(self.solution_type, self.xx)
for var in lp.variables():
var.varValue = self.xx[self.var_dict[var.name]]
except mosek.Error:
pass
# Constraint slack variables.
try:
self.xc = [0.0] * self.numcons
self.task.getxc(self.solution_type, self.xc)
for con in lp.constraints:
lp.constraints[con].slack = -(
self.cons[con].constant + self.xc[self.cons_dict[con]]
)
except mosek.Error:
pass
# Reduced costs.
if self.solution_type != mosek.soltype.itg:
try:
self.x_rc = [0.0] * self.numvars
self.task.getreducedcosts(
self.solution_type, 0, self.numvars, self.x_rc
)
for var in lp.variables():
var.dj = self.x_rc[self.var_dict[var.name]]
except mosek.Error:
pass
# Constraint Pi variables.
try:
self.y = [0.0] * self.numcons
self.task.gety(self.solution_type, self.y)
for con in lp.constraints:
lp.constraints[con].pi = self.y[self.cons_dict[con]]
except mosek.Error:
pass
def putparam(self, par, val):
"""
Pass the values of valid parameters to Mosek.
"""
if isinstance(par, mosek.dparam):
self.task.putdouparam(par, val)
elif isinstance(par, mosek.iparam):
self.task.putintparam(par, val)
elif isinstance(par, mosek.sparam):
self.task.putstrparam(par, val)
elif isinstance(par, str):
if par.startswith("MSK_DPAR_"):
self.task.putnadouparam(par, val)
elif par.startswith("MSK_IPAR_"):
self.task.putnaintparam(par, val)
elif par.startswith("MSK_SPAR_"):
self.task.putnastrparam(par, val)
else:
raise PulpSolverError(
"Invalid MOSEK parameter: '{}'. Check MOSEK documentation for a list of valid parameters.".format(
par
)
)
def actualSolve(self, lp):
"""
Solve a well-formulated lp problem.
"""
self.buildSolverModel(lp)
# Set solver parameters
for msk_par in self.options:
self.putparam(msk_par, self.options[msk_par])
# Task file
if self.task_file_name:
self.task.writedata(self.task_file_name)
# Optimize
self.task.optimize()
# Mosek solver log (default: standard output stream)
if self.msg:
self.task.solutionsummary(mosek.streamtype.msg)
self.findSolutionValues(lp)
lp.assignStatus(self.solution_status_dict[self.solsta])
for var in lp.variables():
var.modified = False
for con in lp.constraints.values():
con.modified = False
return lp.status
def actualResolve(self, lp, inf=1e20, **kwargs):
"""
Modify constraints and re-solve an lp. The Mosek task object created in the first solve is used.
"""
for c in self.cons:
if self.cons[c].modified:
csense = self.cons[c].sense
cconst = -self.cons[c].constant
clow = -inf
cup = inf
# Constraint bounds
if csense == constants.LpConstraintEQ:
cbkey = mosek.boundkey.fx
clow = cconst
cup = cconst
elif csense == constants.LpConstraintGE:
cbkey = mosek.boundkey.lo
clow = cconst
elif csense == constants.LpConstraintLE:
cbkey = mosek.boundkey.up
cup = cconst
else:
raise PulpSolverError("Invalid constraint type.")
self.task.putconbound(self.cons_dict[c], cbkey, clow, cup)
# Re-solve
self.task.optimize()
self.findSolutionValues(lp)
lp.assignStatus(self.solution_status_dict[self.solsta])
for var in lp.variables():
var.modified = False
for con in lp.constraints.values():
con.modified = False
return lp.status
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