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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 561570 of 1277 papers

TitleStatusHype
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules0
A Distribution Evolutionary Algorithm for the Graph Coloring Problem0
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization0
Dynamic Submodular Maximization0
BiGrad: Differentiating through Bilevel Optimization Programming0
Biased Random-Key Genetic Algorithms: A Review0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition0
Dynamic Feature Selection for Dependency Parsing0
Dynamic Assortment Optimization with Changing Contextual Information0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling0
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