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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
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization ChallengesCode2
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning EnvironmentCode2
HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial OptimizationCode2
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic DesignCode2
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex NetworkCode2
LLMOPT: Learning to Define and Solve General Optimization Problems from ScratchCode2
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