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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

Showing 110 of 1277 papers

TitleStatusHype
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-ExpertsCode3
ReEvo: Large Language Models as Hyper-Heuristics with Reflective EvolutionCode3
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language ModelCode3
Diffusion models as plug-and-play priorsCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
Domain-Independent Dynamic ProgrammingCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
Combinatorial Optimization with Automated Graph Neural NetworksCode2
A Diffusion Model Framework for Unsupervised Neural Combinatorial OptimizationCode2
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