Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise-Reference-Cited by-同舟云学术

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Nonlinear Gradient Mappings and Stochastic Optimization: A General Framework with Applications to Heavy-Tail Noise

Published:2023-05-16 Issue:2 Volume:33 Page:394-423
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Jakovetić Dus̆an¹,Bajović Dragana²,Sahu Anit Kumar³,Kar Soummya⁴,Milos̆ević Nemanja¹,Stamenković Dus̆an¹

Affiliation:

1. Faculty of Sciences, Department of Mathematics and Informatics, University of Novi Sad, Novi Sad, 21000, Serbia.

2. Faculty of Technical Sciences, Department of Power, Electronic and Communication Engineering, University of Novi Sad, Novi Sad, 21000, Serbia.

3. Amazon Alexa AI, Seattle, WA USA.

4. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213-3890 USA.

Funder

Horizon 2020 Framework Programme

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software,Applied Mathematics

Reference21 articles.

1. Stable Laws and Domains of Attraction in Free Probability Theory

2. Optimization Methods for Large-Scale Machine Learning

3. On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning

4. Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics

Cited by 2 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. On Some Works of Boris Teodorovich Polyak on the Convergence of Gradient Methods and Their Development;Computational Mathematics and Mathematical Physics;2024-04

2. Nonlinear Consensus+Innovations under Correlated Heavy-Tailed Noises: Mean Square Convergence Rate and Asymptotics;SIAM Journal on Control and Optimization;2024-01-29

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