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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

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Showing 1–25 of 1277 papers

TitleStatusHype
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-ExpertsCode3
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language ModelCode3
ReEvo: Large Language Models as Hyper-Heuristics with Reflective EvolutionCode3
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic DesignCode2
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization ProblemsCode2
LLMOPT: Learning to Define and Solve General Optimization Problems from ScratchCode2
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex NetworkCode2
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial OptimizationCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with PerspectivesCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
Solving the Job Shop Scheduling Problem with Graph Neural Networks: A Customizable Reinforcement Learning EnvironmentCode2
Ant Colony Sampling with GFlowNets for Combinatorial OptimizationCode2
Combinatorial Optimization with Automated Graph Neural NetworksCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Diffusion models as plug-and-play priorsCode2
Domain-Independent Dynamic ProgrammingCode2
A Diffusion Model Framework for Unsupervised Neural Combinatorial OptimizationCode2
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCsCode2
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization ChallengesCode2
Learning to Solve Job Shop Scheduling under UncertaintyCode2
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
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