GraphBU generates MILP instances via graph-native block units that pair local subproblems with explicit coupling interfaces, achieving high structural similarity and feasibility preservation across four MILP families.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
2 Pith papers cite this work, alongside 227 external citations. Polarity classification is still indexing.
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abstract
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural network model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly larger than seen during training. Moreover, we improve for the first time over expert-designed branching rules implemented in a state-of-the-art solver on large problems. Code for reproducing all the experiments can be found at https://github.com/ds4dm/learn2branch.
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A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
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GraphBU: MILP Instance Generation with Graph-Native Block Units
GraphBU generates MILP instances via graph-native block units that pair local subproblems with explicit coupling interfaces, achieving high structural similarity and feasibility preservation across four MILP families.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.