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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Vydáno v:	Journal of mathematical imaging and vision Ročník 66; číslo 3; s. 294 - 313
Hlavní autoři:	Chambolle, Antonin, Delplancke, Claire, Ehrhardt, Matthias J., Schönlieb, Carola-Bibiane, Tang, Junqi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.06.2024 Springer Nature B.V Springer Verlag
Témata:	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
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