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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 9911000 of 1277 papers

TitleStatusHype
Learning chordal extensions0
Generative Neural Network based Spectrum Sharing using Linear Sum Assignment Problems0
Context-Aware Online Adaptation of Mixed Reality Interfaces0
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization0
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman ProblemCode0
Faster width-dependent algorithm for mixed packing and covering LPs0
YaoGAN: Learning Worst-case Competitive Algorithms from Self-generated Inputs0
Solving Packing Problems by Conditional Query Learning0
DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems0
Deep Auto-Deferring Policy for Combinatorial Optimization0
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