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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 7180 of 1277 papers

TitleStatusHype
CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial OptimizationCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task AllocationCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Active Learning Meets Optimized Item SelectionCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
A Bayesian algorithm for retrosynthesisCode1
Automatic Truss Design with Reinforcement LearningCode1
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