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Distributed Stochastic Optimization in Non-differentiable and Non-convex Environments
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Distributed Stochastic Optimization in Non-differentiable and Non-convex Environments. |
| 개인저자 | Vlaski, Stefan. |
| 단체저자명 | University of California, Los Angeles. Electrical and Computer Engineering 0333. |
| 발행사항 | [S.l.]: University of California, Los Angeles., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 285 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-06B. Dissertation Abstract International |
| ISBN | 9781687944665 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Los Angeles, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Advisor: Sayed, Ali H. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | The first part of this dissertation considers distributed learning problems over networked agents. The general objective of distributed adaptation and learning is the solution of global, stochastic optimization problems through localized interactions and without information about the statistical properties of the data.Regularization is a useful technique to encourage or enforce structural properties on the resulting solution, such as sparsity or constraints. A substantial number of regularizers are inherently non-smooth, while many cost functions are differentiable. We propose distributed and adaptive strategies that are able to minimize aggregate sums of objectives. In doing so, we exploit the structure of the individual objectives as sums of differentiable costs and non-differentiable regularizers. The resulting algorithms are adaptive in nature and able to continuously track drifts in the problem |
| 일반주제명 | Electrical engineering. Computer engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
