Recently, I came across the below paper and found it very interesting.
Solving Mixed Integer Programs Using Neural Networks; https://arxiv.org/abs/2012.13349
The idea is to use (train with neural network models) similar problem instances to help the solver finding better incumbent solutions and branching strategies during branch-and-bound.
Has anyone tested this approach on a real world business problem? Is it practical or even worth to invest?
I have some doubts; SCIP is chosen as the base solver and they compare their results with tuned SCIP solver. However, to my experience, SCIP is a much slower solver than commercial solvers like Cplex, Gurobi. I understand the reason why they choose to use SCIP as the base solver as explained in the paper. However, I am still looking for an answer for the below question:
Can this approach will improve the solution time of Cplex solver as much as it does for SCIP solver? Because, SCIP is a much slower solver than Cplex and has more parts to be improved. Would it worth to use such an approach for Cplex?