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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

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Showing 421–430 of 1277 papers

TitleStatusHype
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Causal Effect Identification in Uncertain Causal Networks—0
Entity Summarization: State of the Art and Future Challenges—0
Causal Discovery with Reinforcement Learning—0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models—0
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
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