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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 251260 of 1277 papers

TitleStatusHype
Multi-Robot Connected Fermat Spiral CoverageCode0
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman ProblemsCode0
Combining Gradients and Probabilities for Heterogeneous Approximation of Neural NetworksCode0
Balancing Utility and Fairness in Submodular Maximization (Technical Report)Code0
Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman ProblemCode0
Graph Adversarial Immunization for Certifiable RobustnessCode0
Balanced Crossover Operators in Genetic AlgorithmsCode0
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF SignalsCode0
Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex NetworksCode0
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume ImagesCode0
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