Minibatch stochastic subgradient-based projection algorithms for feasibility problems with convex inequalities-Reference-Cited by-同舟云学术

Minibatch stochastic subgradient-based projection algorithms for feasibility problems with convex inequalities

Published:2021-06-28 Issue:1 Volume:80 Page:121-152
ISSN:0926-6003
Container-title:Computational Optimization and Applications
language:en
Short-container-title:Comput Optim Appl

Author:

Necoara Ion^ORCID,Nedić Angelia

Funder

Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Control and Optimization

Link

https://link.springer.com/content/pdf/10.1007/s10589-021-00294-3.pdf

Reference36 articles.

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2. Bauschke, H.H.: Projection algorithms: Results and open problems. In: Butnariu, D., Censor, Y., Reich, S. (eds.) Inherently Parallel Algorithms in Feasibility and Optimization and their Applications, pp. 11–22. Elsevier, Amsterdam (2001)

3. Bauschke, H.H., Combettes, P.L., Luke, D.R.: Hybrid projection-reflection method for phase retrieval. J. Opt. Soc. Am. 20(6), 1025–1034 (2003)

4. Boyd, S., El Ghaoui, L., Feron, E., Balakrishnan, V.: Linear Matrix Inequalities in Systems and Control Theory. SIAM, Philadelphia (1994)

5. Bregman, L.M.: The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. Zh. Vychisl. Mat. Mat. Fiz. 7, 620–631 (1967)

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