This paper surveys the recent attempts at leveraging machine learning to solve constrained
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …

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 …

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 …

Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact
algorithms, making them a tempting domain to apply machine learning methods. The highly …

Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …

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

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 …

Branch-and-bound approaches in integer programming require ordering portions of the
space to explore next, a problem known as node comparison. We propose a new siamese …

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