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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 511–520 of 1277 papers

TitleStatusHype
Enhancing variational quantum algorithms by balancing training on classical and quantum hardware—0
Causal Effect Identification in Uncertain Causal Networks—0
Enhancing Robustness of Neural Networks through Fourier Stabilization—0
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion—0
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
AED: An Anytime Evolutionary DCOP Algorithm—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update—0
Cakewalk Sampling—0
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