Tightening Discretization-based MILP Models for the Pooling Problem using Upper Bounds on Bilinear Terms
Optimization and Control
2023-01-18 v2 Machine Learning
Abstract
Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem. These methods convert the original nonconvex optimization problems into mixed-integer linear programs (MILPs). In this paper we study tightening methods for these MILP models for the pooling problem, and derive valid constraints using upper bounds on bilinear terms. Computational results demonstrate the effectiveness of our methods in terms of reducing solution time.
Cite
@article{arxiv.2207.03699,
title = {Tightening Discretization-based MILP Models for the Pooling Problem using Upper Bounds on Bilinear Terms},
author = {Yifu Chen and Christos T. Maravelias and Xiaomin Zhang},
journal= {arXiv preprint arXiv:2207.03699},
year = {2023}
}