Semi-discrete optimal transport: hardness, regularization and numerical solution

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems are ubiquitous in statistics, machine learning and computer vision, howev...

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Vydáno v:	Mathematical programming Ročník 199; číslo 1-2; s. 1033 - 1106
Hlavní autoři:	Taşkesen, Bahar, Shafieezadeh-Abadeh, Soroosh, Kuhn, Daniel
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023 Springer
Témata:	Algorithms Calculus of Variations and Optimal Control; Optimization Combinatorics Full Length Paper Hardness Machine learning Machine vision Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Mechanical properties Numerical Analysis Theoretical Stochastic gradient descent algorithms P-hardness Discrete choice models Distributionally robust optimization Wasserstein distance Optimal transport Complexity
ISSN:	0025-5610, 1436-4646
On-line přístup:	Získat plný text
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