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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
A topological analysis of the space of recipes0
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization0
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
Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization0
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems0
Combining Learned Representations for Combinatorial Optimization0
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning0
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