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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

Showing 941950 of 1277 papers

TitleStatusHype
Natural evolution strategies and variational Monte CarloCode0
A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem0
Enriching Documents with Compact, Representative, Relevant Knowledge GraphsCode0
Multi-layer local optima networks for the analysis of advanced local search-based algorithms0
Versatile Black-Box Optimization0
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?0
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem0
Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle0
Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem0
Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach0
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