Approximate Dual Averaging Method for Multiagent Saddle-Point Problems with Stochastic Subgradients
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-02
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
This paper considers the problem of solving the saddle-point problem over a network, which consists of multiple interacting agents.
The global objective function of the problem is a combination of local convex-concave functions, each of which is only available to one agent.
Our main focus is on the case where the projection steps are calculated approximately and the subgradients are corrupted by some stochastic noises.
We propose an approximate version of the standard dual averaging method and show that the standard convergence rate is preserved, provided that the projection errors decrease at some appropriate rate and the noises are zero-mean and have bounded variance.
American Psychological Association (APA)
Yuan, Deming& Yang, Yang. 2014. Approximate Dual Averaging Method for Multiagent Saddle-Point Problems with Stochastic Subgradients. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1044077
Modern Language Association (MLA)
Yuan, Deming& Yang, Yang. Approximate Dual Averaging Method for Multiagent Saddle-Point Problems with Stochastic Subgradients. Mathematical Problems in Engineering No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1044077
American Medical Association (AMA)
Yuan, Deming& Yang, Yang. Approximate Dual Averaging Method for Multiagent Saddle-Point Problems with Stochastic Subgradients. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1044077
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044077