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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 181190 of 1277 papers

TitleStatusHype
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Graph Neural Networks for Maximum Constraint SatisfactionCode1
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
Attention, Learn to Solve Routing Problems!Code1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
Neural Combinatorial Optimization with Reinforcement LearningCode1
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
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