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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Bibliographic Details
Published in:	Mathematical programming Vol. 199; no. 1-2; pp. 1033 - 1106
Main Authors:	Taşkesen, Bahar, Shafieezadeh-Abadeh, Soroosh, Kuhn, Daniel
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023 Springer
Subjects:	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
Online Access:	Get full text
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