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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 12611270 of 1277 papers

TitleStatusHype
GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning0
Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs0
Gumbel-softmax Optimization: A Simple General Framework for Combinatorial Optimization Problems on Graphs0
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization0
Hardness of Online Sleeping Combinatorial Optimization Problems0
Heed the Noise in Performance Evaluations in Neural Architecture Search0
Heuristic with elements of tabu search for Truck and Trailer Routing Problem0
Arbitrarily Large Labelled Random Satisfiability Formulas for Machine Learning Training0
Hierarchical Clustering: Objective Functions and Algorithms0
High-Dimensional Prediction for Sequential Decision Making0
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