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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 501510 of 1277 papers

TitleStatusHype
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms0
CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models0
Entity Summarization: State of the Art and Future Challenges0
Fuzzy Integer Linear Programming Mathematical Models for Examination Timetable Problem0
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization0
GenCO: Generating Diverse Designs with Combinatorial Constraints0
Causal Discovery with Reinforcement Learning0
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems0
An interacting replica approach applied to the traveling salesman problem0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware0
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