combinatorial optimization
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Variational Annealing on Graphs for Combinatorial Optimization
We demonstrate that this assumption imposes performance limitations in particular on difficult problem instances.
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Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization Jinbiao Chen
Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MO-COPs).
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85db52cc08c5e00cfb1d216b1c85ba35-Paper-Conference.pdf
Concretely, we prove that polynomial-sized message-passing GNN's can learn the
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Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
In practice, most combinatorial optimization problems can be formulated as mixed-integer linear programs (MILPs), in which case branch-and-bound (B&B) [35] is the exact method of choice.
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