An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

In the past few years, there has been an explosive surge in the use of machine learning (ML)
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …

State-of-the-art Mixed Integer Linear Programming (MILP) solvers combine systematic tree
search with a plethora of hard-coded heuristics, such as branching rules. While approaches …

Recently, machine learning, particularly message-passing graph neural networks (MPNNs),
has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed …

Learning to optimize is a rapidly growing area that aims to solve optimization problems or
improve existing optimization algorithms using machine learning (ML). In particular, the …

Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …

We survey recent work on machine learning (ML) techniques for selecting cutting planes (or
cuts) in mixed-integer linear programming (MILP). Despite the availability of various classes …

Abstract Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization
that offers a powerful modeling language for a wide range of applications. The main engine …

The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN)
have been shown to be an important component of deep learning based Mixed-Integer …