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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 2130 of 1277 papers

TitleStatusHype
Diffusion models as plug-and-play priorsCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Structured Reinforcement Learning for Combinatorial Decision-MakingCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
Neural Combinatorial Optimization for Real-World RoutingCode1
FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data ClassificationCode1
Starjob: Dataset for LLM-Driven Job Shop SchedulingCode1
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop SchedulingCode1
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