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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 861870 of 1277 papers

TitleStatusHype
From Local Structures to Size Generalization in Graph Neural Networks0
Binary matrix factorization on special purpose hardware0
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm0
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking0
An Efficient Algorithm for Cooperative Semi-Bandits0
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks0
On Size Generalization in Graph Neural Networks0
Complex Vehicle Routing with Memory Augmented Neural Networks0
On combinatorial optimization for dominating sets (literature survey, new models)0
LeadCache: Regret-Optimal Caching in NetworksCode0
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