Abstract
In this paper, we propose distributed optimization methods to solve systems of linear equations. We provide convergence analysis for both continuous and discrete time computation models based on linear systems theory. It is shown that the proposed computation approaches work for very general linear equations, scalable with data sets and can be implemented in distributed or parallel fashion. Furthermore, we show that the discrete time algorithm admits constant update step size in the presence of additive uncertainties. This robustness feature makes the approach computationally efficient and supplementary to the existing approaches to deal with uncertainties such as stochastic (sub-)gradient methods and sample averaging.
| Original language | English (US) |
|---|---|
| Title of host publication | 19th IFAC World Congress IFAC 2014, Proceedings |
| Editors | Edward Boje, Xiaohua Xia |
| Publisher | IFAC Secretariat |
| Pages | 1210-1215 |
| Number of pages | 6 |
| ISBN (Electronic) | 9783902823625 |
| DOIs | |
| State | Published - 2014 |
| Externally published | Yes |
| Event | 19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 - Cape Town, South Africa Duration: Aug 24 2014 → Aug 29 2014 |
Publication series
| Name | IFAC Proceedings Volumes (IFAC-PapersOnline) |
|---|---|
| Volume | 19 |
| ISSN (Print) | 1474-6670 |
Other
| Other | 19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 |
|---|---|
| Country/Territory | South Africa |
| City | Cape Town |
| Period | 8/24/14 → 8/29/14 |
Bibliographical note
Publisher Copyright:© IFAC.
Keywords
- Additive uncertainties
- Distributed and parallel computation
- Distributed optimization
- Noises
- Stochastic programming
- Systems of linear equations