Mixed-integer programming (MIP) technology offers a generic way of formulating and
solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

Abstract Mixed Integer Programming (MIP) is one of the most widely used modeling
techniques for combinatorial optimization problems. In many applications, a similar MIP …

Primal heuristics play a crucial role in exact solvers for Mixed Integer Programming (MIP).
While solvers are guaranteed to find optimal solutions given sufficient time, real-world …

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 …

In this chapter, we provide an overview of the current state of the art with respect to solution
of mixed integer linear optimization problems (MILPs) in parallel. Sequential algorithms for …

This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing
machine learning techniques. Mixed Integer Programming is a general technique for …

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

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