Highs with python-mip is slow

[colonetti]

Hello, everyone

Firstly, thanks for the great package and also for adding support for Highs.

However, it seems that creating a optimization model with Highs is rather slow.

Describe the bug
Adding variables to a Highs optimization model is slow.

To Reproduce

import sys
import mip as pm
from timeit import default_timer as dt

def main(solver):
    m = pm.Model(solver_name=solver)
    ini = dt()
    x = {i: m.add_var() for i in range(10000)}
    print(f"{dt() - ini} seconds to add vars", flush=True)

if __name__ == '__main__':
    main(sys.argv[1])

Expected behavior
GRB: 0.021987227955833077 seconds to add vars
CBC: 0.026054809102788568 seconds to add vars
Highs: 6.408406416885555 seconds to add vars

Desktop (please complete the following information):

• Operating System, version: Ubuntu 23.10
• Python version: 3.11.6
• Python-MIP version (we recommend you to test with the latest version): 1.16tc0

Thanks again!

[rschwarz]

I have not looked into the code in a long while, but since the above call to add_var passes neither a name nor a variable type (integrality), the python-mip wrapper doesn't do much beyond calling Highs_addCol in a loop (see https://github.com/coin-or/python-mip/blob/master/mip/highs.py#L809). I'm not sure why this takes a lot of time, or how this could be avoided, because models in python-mip are created incrementally, not in bulk (vectorized), right? :-/

EDIT: Maybe the call to Highs_getNumCol is expensive and we could cache this count on the Python side. See also the implementation in the Julia/MOI wrapper for comparison: https://github.com/jump-dev/HiGHS.jl/blob/master/src/MOI_wrapper.jl#L738

[colonetti]

I have not looked into the code in a long while, but since the above call to add_var passes neither a name nor a variable type (integrality), the python-mip wrapper doesn't do much beyond calling Highs_addCol in a loop (see https://github.com/coin-or/python-mip/blob/master/mip/highs.py#L809). I'm not sure why this takes a lot of time, or how this could be avoided, because models in python-mip are created incrementally, not in bulk (vectorized), right? :-/

EDIT: Maybe the call to Highs_getNumCol is expensive and we could cache this count on the Python side. See also the implementation in the Julia/MOI wrapper for comparison: https://github.com/jump-dev/HiGHS.jl/blob/master/src/MOI_wrapper.jl#L738

Thanks, @rschwarz. I'll take a look into it.

[metab0t]

@rschwarz

The solution is to avoid using Highs_passColName and Highs_passRowName. They are quite expensive and should not be called for each variable/column.

The name of variable is set automatically at

python-mip/mip/lists.py

Line 40 in 0ccb811

name = "var({})".format(len(self.__vars))

although the user does not specify the name.

I met the same problem in PyOptInterface and decide to maintain the name mapping internally: metab0t/PyOptInterface@0074dc0

Update: A fix is proposed at ERGO-Code/HiGHS#1782 to solve this problem.