I want to set the optimality gap when calculating a solution between the optimal and the actual solution.
I use PuLP version 1.6.1 and I want to pass the parameter of gap to the solver. Does anyone have an example or an idea on how to approach this issue.
The documentation that can be found here:
https://pythonhosted.org/PuLP/solvers.html
Is not very helpful.
Thank you.
The documentation exactly shows you how to pass options to the solvers. Maybe not all of cbcs options are supported, but pulp's code shows, that the task you want is handled by the argument fracGap.
class COIN_CMD(LpSolver_CMD):
"""The COIN CLP/CBC LP solver
now only uses cbc
"""
def defaultPath(self):
return self.executableExtension(cbc_path)
def __init__(self, path = None, keepFiles = 0, mip = 1,
msg = 0, cuts = None, presolve = None, dual = None,
strong = None, options = [],
fracGap = None, maxSeconds = None, threads = None)
Let's take one pulp's examples from here.
"""
The Computer Plant Problem for the PuLP Modeller
Authors: Antony Phillips, Dr Stuart Mitchell 2007
"""
# Import PuLP modeler functions
from pulp import *
# Creates a list of all the supply nodes
Plants = ["San Francisco",
"Los Angeles",
"Phoenix",
"Denver"]
# Creates a dictionary of lists for the number of units of supply at
# each plant and the fixed cost of running each plant
supplyData = {#Plant Supply Fixed Cost
"San Francisco":[1700, 70000],
"Los Angeles" :[2000, 70000],
"Phoenix" :[1700, 65000],
"Denver" :[2000, 70000]
}
# Creates a list of all demand nodes
Stores = ["San Diego",
"Barstow",
"Tucson",
"Dallas"]
# Creates a dictionary for the number of units of demand at each store
demand = { #Store Demand
"San Diego":1700,
"Barstow" :1000,
"Tucson" :1500,
"Dallas" :1200
}
# Creates a list of costs for each transportation path
costs = [ #Stores
#SD BA TU DA
[5, 3, 2, 6], #SF
[4, 7, 8, 10],#LA Plants
[6, 5, 3, 8], #PH
[9, 8, 6, 5] #DE
]
# Creates a list of tuples containing all the possible routes for transport
Routes = [(p,s) for p in Plants for s in Stores]
# Splits the dictionaries to be more understandable
(supply,fixedCost) = splitDict(supplyData)
# The cost data is made into a dictionary
costs = makeDict([Plants,Stores],costs,0)
# Creates the problem variables of the Flow on the Arcs
flow = LpVariable.dicts("Route",(Plants,Stores),0,None,LpInteger)
# Creates the master problem variables of whether to build the Plants or not
build = LpVariable.dicts("BuildaPlant",Plants,0,1,LpInteger)
# Creates the 'prob' variable to contain the problem data
prob = LpProblem("Computer Plant Problem",LpMinimize)
# The objective function is added to prob - The sum of the transportation costs and the building fixed costs
prob += lpSum([flow[p][s]*costs[p][s] for (p,s) in Routes])+lpSum([fixedCost[p]*build[p] for p in Plants]),"Total Costs"
# The Supply maximum constraints are added for each supply node (plant)
for p in Plants:
prob += lpSum([flow[p][s] for s in Stores])<=supply[p]*build[p], "Sum of Products out of Plant %s"%p
# The Demand minimum constraints are added for each demand node (store)
for s in Stores:
prob += lpSum([flow[p][s] for p in Plants])>=demand[s], "Sum of Products into Stores %s"%s
# The problem data is written to an .lp file
prob.writeLP("ComputerPlantProblem.lp")
# The problem is solved using PuLP's choice of Solver
prob.solve(PULP_CBC_CMD())
# The status of the solution is printed to the screen
print("Status:", LpStatus[prob.status])
# Each of the variables is printed with it's resolved optimum value
for v in prob.variables():
print(v.name, "=", v.varValue)
# The optimised objective function value is printed to the screen
print("Total Costs = ", value(prob.objective))
...
('Total Costs = ', 228100.0)
#prob.solve(PULP_CBC_CMD()) # old call
prob.solve(PULP_CBC_CMD(fracGap = 0.1))
...
('Total Costs = ', 230300.0)
