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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 241250 of 1277 papers

TitleStatusHype
Graph Adversarial Immunization for Certifiable RobustnessCode0
Global Optimal Path-Based Clustering AlgorithmCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman ProblemCode0
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing SolverCode0
Multi-objective Pointer Network for Combinatorial OptimizationCode0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Bayesian Optimization of Functions over Node Subsets in GraphsCode0
Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman ProblemCode0
Futureproof Static Memory PlanningCode0
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