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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 281290 of 1277 papers

TitleStatusHype
Implementation of digital MemComputing using standard electronic componentsCode0
Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set ProgrammingCode0
Fast Parallel Algorithms for Statistical Subset Selection ProblemsCode0
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume ImagesCode0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin SplittingCode0
Fair Correlation ClusteringCode0
All-to-all reconfigurability with sparse and higher-order Ising machinesCode0
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
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