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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 1120 of 1277 papers

TitleStatusHype
Ant Colony Sampling with GFlowNets for Combinatorial OptimizationCode2
Domain-Independent Dynamic ProgrammingCode2
Combinatorial Optimization with Automated Graph Neural NetworksCode2
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic DesignCode2
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Diffusion models as plug-and-play priorsCode2
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial OptimizationCode2
A Diffusion Model Framework for Unsupervised Neural Combinatorial OptimizationCode2
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex NetworkCode2
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