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Found 9 papers, 7 papers with code
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.
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
Branch-and-bound approaches in integer programming require ordering portions of the space to explore next, a problem known as node comparison.
Learning to branch with Tree MDPs
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
Ecole: A Library for Learning Inside MILP Solvers
In this paper we describe Ecole (Extensible Combinatorial Optimization Learning Environments), a library to facilitate integration of machine learning in combinatorial optimization solvers.
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer science.
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
We present Ecole, a new library to simplify machine learning research for combinatorial optimization.
Change Point Detection by Cross-Entropy Maximization
Many offline unsupervised change point detection algorithms rely on minimizing a penalized sum of segment-wise costs.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.
