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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 1171–1180 of 1277 papers

TitleStatusHype
End-to-end Planning of Fixed Millimeter-Wave Networks—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems—0
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Entity Summarization: State of the Art and Future Challenges—0
Equivariant neural networks for recovery of Hadamard matrices—0
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