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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 611620 of 1277 papers

TitleStatusHype
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles0
Unsupervised Learning for Combinatorial Optimization Needs Meta-LearningCode0
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem0
PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching0
A machine learning framework for neighbor generation in metaheuristic search0
A Local Optima Network View of Real Function Fitness Landscapes0
Learning to repeatedly solve routing problems0
Local Branching Relaxation Heuristics for Integer Linear Programs0
Security Defense of Large Scale Networks Under False Data Injection Attacks: An Attack Detection Scheduling Approach0
Walkability Optimization: Formulations, Algorithms, and a Case Study of TorontoCode0
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