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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

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Showing 871–880 of 1277 papers

TitleStatusHype
Deep Momentum Uncertainty Hashing—0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Cost-aware Feature Selection for IoT Device Classification—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
Differentiable TAN Structure Learning for Bayesian Network ClassifiersCode0
A Survey on Reinforcement Learning for Combinatorial Optimization—0
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network—0
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning—0
Learning (Re-)Starting Solutions for Vehicle Routing Problems—0
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