Machine learning for combinatorial optimization: a methodological tour d'horizon
This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …
research communities, at leveraging machine learning to solve combinatorial optimization …
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 …
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 …
Exact combinatorial optimization with graph convolutional neural networks
Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …
paradigm. We propose a new graph convolutional neural network model for learning branch …
Learning combinatorial optimization algorithms over graphs
The design of good heuristics or approximation algorithms for NP-hard combinatorial
optimization problems often requires significant specialized knowledge and trial-and-error …
optimization problems often requires significant specialized knowledge and trial-and-error …
Learning heuristics for the tsp by policy gradient
The aim of the study is to provide interesting insights on how efficient machine learning
algorithms could be adapted to solve combinatorial optimization problems in conjunction …
algorithms could be adapted to solve combinatorial optimization problems in conjunction …
Optimization problems for machine learning: A survey
This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …
framework several commonly used machine learning approaches. Particularly …
Simulation-guided beam search for neural combinatorial optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to
discover powerful heuristics for solving complex real-world problems. While neural …
discover powerful heuristics for solving complex real-world problems. While neural …
Branch and bound for piecewise linear neural network verification
The success of Deep Learning and its potential use in many safety-critical applications has
motivated research on formal verification of Neural Network (NN) models. In this context …
motivated research on formal verification of Neural Network (NN) models. In this context …
Hybrid models for learning to branch
Abstract A recent Graph Neural Network (GNN) approach for learning to branch has been
shown to successfully reduce the running time of branch-and-bound algorithms for Mixed …
shown to successfully reduce the running time of branch-and-bound algorithms for Mixed …