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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 341350 of 1277 papers

TitleStatusHype
Density Maximization in Context-Sense Metric Space for All-words WSD0
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint0
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem0
Design And Optimization Of Multi-rendezvous Manoeuvres Based On Reinforcement Learning And Convex Optimization0
Design Space Exploration as Quantified Satisfaction0
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection0
Detecting Overlapping Temporal Community Structure in Time-Evolving Networks0
Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems0
Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture0
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