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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 311320 of 1277 papers

TitleStatusHype
Fair Correlation ClusteringCode0
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
Co-training for Policy LearningCode0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Implementing a GPU-based parallel MAX-MIN Ant SystemCode0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman ProblemsCode0
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