Cplex.linear_constraints.add Too Slow For Large Models

I've been trying to use cplex to solve an optimal transportation problem. The problem model is normally very large (in my description below the total number of variables are 104857

Solution 1:

You will get much better performance using indices rather than names. This is discussed in the documentation here.

Here is a modified version of your example (just the model building part) that uses indices:

from __future__ import print_function
import cplex

DOT = cplex.Cplex()
DOT.objective.set_sense(DOT.objective.sense.minimize)

size = 1024
C = [1.0] * (size * size)  # dummy data# set up names of variables
nms = ["x{0}".format(i) for i in range(size * size)]
# add variables to the model and store indices as a list
nmindices = list(DOT.variables.add(obj = C, names = nms))

constraints = list()
for i in range(size):
    constraints.append([nmindices[i * size : (i + 1) * size], [1.0] * size])

for i in range(size):
    constraints.append(cplex.SparsePair(nmindices[i : size * size : size], [1.0] * size))
rhs = [1.0] * (size * 2)  # dummy data
constraint_senses = ["E"] * (size * 2)
# the following line: adding constraints to model is faster now
DOT.linear_constraints.add(lin_expr = constraints, senses = constraint_senses, rhs = rhs)
# write out the model in LP format for debugging
DOT.write("test.lp")