End-to-end constrained optimization learning: A survey
J Kotary, F Fioretto, P Van Hentenryck… - arXiv preprint arXiv …, 2021 - arxiv.org
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 …
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …
A review on learning to solve combinatorial optimisation problems in manufacturing
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 …
rapid development of science and technology and the progress of human society, the …
Combinatorial optimization and reasoning with graph neural networks
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 …
science. Until recently, its methods have focused on solving problem instances in isolation …
Solving mixed integer programs using neural networks
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 …
developed with decades of research to solve large-scale MIP instances encountered in …
Let the flows tell: Solving graph combinatorial problems with gflownets
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 …
algorithms, making them a tempting domain to apply machine learning methods. The highly …
Network planning with deep reinforcement learning
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 …
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …
A survey for solving mixed integer programming via machine learning
Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
Learning to branch with tree mdps
L Scavuzzo, F Chen, D Chételat… - Advances in neural …, 2022 - proceedings.neurips.cc
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 …
search with a plethora of hard-coded heuristics, such as branching rules. While approaches …
Learning to compare nodes in branch and bound with graph neural networks
AG Labassi, D Chételat, A Lodi - Advances in neural …, 2022 - proceedings.neurips.cc
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 …
space to explore next, a problem known as node comparison. We propose a new siamese …
Exploring the power of graph neural networks in solving linear optimization problems
Recently, machine learning, particularly message-passing graph neural networks (MPNNs),
has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed …
has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed …