图 书 馆
平均算法与分布式优化Averaging algorithms and distributed optimization |
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| 课程网址: | http://videolectures.net/nipsworkshops2010_tsitsiklis_aad/ |
| 主讲教师: | John N. Tsitsiklis |
| 开课单位: | 麻省理工学院 |
| 开课时间: | 2011-01-13 |
| 课程语种: | 英语 |
| 中文简介: |  在分布式平均算法和共识算法中，处理器通过与邻居的值形成局部平均值来交换和更新某些值（或“估计”或“观点”）。在合适的条件下，这样的算法收敛到共识（每个处理器最终都保持相同的值）或平均共识（共识是在处理器所拥有的初始值的平均值上实现的）。已经提出了这种算法作为分布式优化方法的子例程，用于在主算法运行时组合不同处理器的结果。我们概述了平均算法的一些应用，重点放在诸如优化方法之类的梯度上。然后，我们着重介绍一些新旧结果，重点是收敛速度。我们终于讨论了一些未解决的问题。 |
| 课程简介: | In distributed averaging and consensus algorithms, processors exchange and update certain values (or "estimates", or "opinions") by forming a local average with the values of their neighbors. Under suitable conditions, such algorithms converge to consensus (every processor ends up holding the same value) or even average-consensus (consensus is achieved on the average of the initial values held by the processors). Algorithms of this type have been proposed as a subroutine of distributed optimization methods, used to combine the results of different processors while a master algorithm is running. We overview a few applications of averaging algorithms, with a focus on gradient-like optimization methods. We then proceed to highlight some results, old and new, with a focus on convergence rates. We finally discuss some open problems. |
| 关 键 词: | 处理器; 平均算法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-03：zyk |
| 最后编审: | 2020-12-03：zyk |
| 阅读次数: | 72 |