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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 721730 of 1277 papers

TitleStatusHype
RITFIS: Robust input testing framework for LLMs-based intelligent software0
Robust Correlation Clustering with Asymmetric Noise0
Robust Estimation of Regression Models with Potentially Endogenous Outliers via a Modern Optimization Lens0
An LP-Based Approach for Goal Recognition as Planning0
Rough matroids based on coverings0
Route Planning Using Nature-Inspired Algorithms0
Routing Arena: A Benchmark Suite for Neural Routing Solvers0
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem0
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem0
Safe Element Screening for Submodular Function Minimization0
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