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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
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model—0
Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location—0
Learning to Learn with Quantum Optimization via Quantum Neural Networks—0
Learning to Make Decisions via Submodular Regularization—0
Maximizing Influence with Graph Neural Networks—0
Learning to Order Graph Elements with Application to Multilingual Surface Realization—0
Learning to Propose Objects—0
Learning to Read through Machine Teaching—0
Learning to repeatedly solve routing problems—0
Learning to Retrieve Iteratively for In-Context Learning—0
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