MIPaaL differentiates through mixed integer programs via cutting planes to enable decision-focused learning for general MIPs, outperforming two-stage prediction-plus-optimization and LP-relaxation baselines on real-world domains.
Attention, learn to solve routing problems! In International Conference on Learning Representations
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The CARM module boosts neural routing solvers by adaptively modulating embeddings with constraint variables, enabling better use of global observations and improved performance on constrained VRPs.
HMACE deploys Proposer, Generator, Evaluator, and Reflector agents in an evolutionary loop to generate and refine heuristics for NP-hard problems, reporting lower optimality gaps and token costs than baselines on TSP and Online BPP.
citing papers explorer
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MIPaaL: Mixed Integer Program as a Layer
MIPaaL differentiates through mixed integer programs via cutting planes to enable decision-focused learning for general MIPs, outperforming two-stage prediction-plus-optimization and LP-relaxation baselines on real-world domains.
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Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver
The CARM module boosts neural routing solvers by adaptively modulating embeddings with constraint variables, enabling better use of global observations and improved performance on constrained VRPs.
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HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization
HMACE deploys Proposer, Generator, Evaluator, and Reflector agents in an evolutionary loop to generate and refine heuristics for NP-hard problems, reporting lower optimality gaps and token costs than baselines on TSP and Online BPP.