Fast distributed first-order methods
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2012
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| Online Access: | http://hdl.handle.net/1721.1/75628 |
| _version_ | 1826192087283924992 |
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| author | Chen, Annie I-An |
| author2 | Asuman Ozdaglar. |
| author_facet | Asuman Ozdaglar. Chen, Annie I-An |
| author_sort | Chen, Annie I-An |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. |
| first_indexed | 2024-09-23T09:06:04Z |
| format | Thesis |
| id | mit-1721.1/75628 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:06:04Z |
| publishDate | 2012 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/756282019-04-10T20:45:43Z Fast distributed first-order methods Chen, Annie I-An Asuman Ozdaglar. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 91-94). This thesis provides a systematic framework for the development and analysis of distributed optimization methods for multi-agent networks with time-varying connectivity. The goal is to optimize a global objective function which is the sum of local objective functions privately known to individual agents. In our methods, each agent iteratively updates its estimate of the global optimum by optimizing its local function and exchanging estimates with others in the network. We introduce distributed proximal-gradient methods that enable the use of a gradient-based scheme for non-differentiable functions with a favorable structure. We present a convergence rate analysis that highlights the dependence on the step size rule. We also propose a novel fast distributed method that uses Nesterov-type acceleration techniques and multiple communication steps per iteration. Our method achieves exact convergence at the rate of O(1/t) (where t is the number of communication steps taken), which is superior than the rates of existing gradient or subgradient algorithms, and is confirmed by simulation results. by I-An Chen. S.M. 2012-12-13T18:47:23Z 2012-12-13T18:47:23Z 2012 2012 Thesis http://hdl.handle.net/1721.1/75628 818184937 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 94 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Chen, Annie I-An Fast distributed first-order methods |
| title | Fast distributed first-order methods |
| title_full | Fast distributed first-order methods |
| title_fullStr | Fast distributed first-order methods |
| title_full_unstemmed | Fast distributed first-order methods |
| title_short | Fast distributed first-order methods |
| title_sort | fast distributed first order methods |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/75628 |
| work_keys_str_mv | AT chenannieian fastdistributedfirstordermethods |