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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 371380 of 1277 papers

TitleStatusHype
Addressing The Knapsack Challenge Through Cultural Algorithm Optimization0
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?0
Large Language Models as Evolutionary OptimizersCode1
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network0
High-Dimensional Prediction for Sequential Decision Making0
Interferometric Neural NetworksCode0
Neural Multi-Objective Combinatorial Optimization with Diversity EnhancementCode1
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and PruningCode1
Neural Packing: from Visual Sensing to Reinforcement Learning0
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
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