Stochastic Primal–Dual Hybrid Gradient Algorithm with Adaptive Step Sizes

In this work, we propose a new primal–dual algorithm with adaptive step sizes. The stochastic primal–dual hybrid gradient (SPDHG) algorithm with constant step sizes has become widely applied in large-scale convex optimization across many scientific fields due to its scalability. While the product of...

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Veröffentlicht in:	Journal of mathematical imaging and vision Jg. 66; H. 3; S. 294 - 313
Hauptverfasser:	Chambolle, Antonin, Delplancke, Claire, Ehrhardt, Matthias J., Schönlieb, Carola-Bibiane, Tang, Junqi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2024 Springer Nature B.V Springer Verlag
Schlagworte:	Adaptive algorithms Algorithms Applications of Mathematics Computed tomography Computer Science Convergence Convex analysis Convexity Image Processing and Computer Vision Inverse problems Mathematical Methods in Physics Mathematics Signal,Image and Speech Processing Upper bounds
ISSN:	0924-9907, 1573-7683
Online-Zugang:	Volltext
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