Randomized Methods for Computing Optimal Transport Without Regularization and Their Convergence Analysis

The optimal transport (OT) problem can be reduced to a linear programming (LP) problem through discretization. In this paper, we introduced the random block coordinate descent (RBCD) methods to directly solve this LP problem. Our approach involves restricting the potentially large-scale optimization...

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Veröffentlicht in:	Journal of scientific computing Jg. 100; H. 2; S. 37
Hauptverfasser:	Xie, Yue, Wang, Zhongjian, Zhang, Zhiwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2024 Springer Nature B.V
Schlagworte:	Algorithms Computational Mathematics and Numerical Analysis Convergence Data science Efficiency Entropy Experiments Linear programming Linear systems Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Methods Optimization Partial differential equations Performance evaluation Regularization Theoretical Deep particle method 68W20 65C35 Convex optimization 90C25 Random block coordinate descent Optimal transport 90C08 Convergence analysis
ISSN:	0885-7474, 1573-7691
Online-Zugang:	Volltext
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