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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 5160 of 1277 papers

TitleStatusHype
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot GeneralizationCode1
Moco: A Learnable Meta Optimizer for Combinatorial OptimizationCode1
Variational Annealing on Graphs for Combinatorial OptimizationCode1
Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization RegimeCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Large Language Models as Evolutionary OptimizersCode1
Neural Multi-Objective Combinatorial Optimization with Diversity EnhancementCode1
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and PruningCode1
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale GeneralizationCode1
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