A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction-Reference-Cited by-同舟云学术

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A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction

Published:2024-03-26 Issue:1 Volume:34 Page:1157-1185
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Milzarek Andre¹^ORCID,Schaipp Fabian²^ORCID,Ulbrich Michael²

Affiliation:

1. School of Data Science, The Chinese University of Hong Kong, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.

2. Chair of Mathematical Optimization, Department of Mathematics, Technical University of Munich, Munich, Germany.

Funder

Fundamental Research Fund–Shenzhen Research Institute of Big Data

Shenzhen Science and Technology Program

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference57 articles.

1. Robust inversion, dimensionality reduction, and randomized sampling

2. The importance of better models in stochastic optimization

3. Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity

4. Convex Analysis and Monotone Operator Theory in Hilbert Spaces

5. Incremental proximal methods for large scale convex optimization

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Variance Reduction Techniques for Stochastic Proximal Point Algorithms;Journal of Optimization Theory and Applications;2024-08-02

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