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 …
A deep instance generative framework for milp solvers under limited data availability
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 …
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …
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 …
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 …
On representing linear programs by graph neural networks
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 …
improve existing optimization algorithms using machine learning (ML). In particular, the …
Learning to optimize: A tutorial for continuous and mixed-integer optimization
Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …
learning, utilizing the capabilities of machine learning to enhance conventional optimization …
Machine learning for cutting planes in integer programming: A survey
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 …
cuts) in mixed-integer linear programming (MILP). Despite the availability of various classes …
Lookback for learning to branch
The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN)
have been shown to be an important component of deep learning based Mixed-Integer …
have been shown to be an important component of deep learning based Mixed-Integer …
Learning to dive in branch and bound
M Paulus, A Krause - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Primal heuristics are important for solving mixed integer linear programs, because they find
feasible solutions that facilitate branch and bound search. A prominent group of primal …
feasible solutions that facilitate branch and bound search. A prominent group of primal …