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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 1111–1120 of 1277 papers

TitleStatusHype
Deep Momentum Uncertainty Hashing—0
Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Deep Reinforcement Learning for Modelling Protein Complexes—0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems—0
Density Maximization in Context-Sense Metric Space for All-words WSD—0
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