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乘子交替方向法

Alternating Direction Method of Multipliers
课程网址: http://videolectures.net/nipsworkshops2011_boyd_multipliers/  
主讲教师: Stephen P. Boyd
开课单位: 斯坦福大学
开课时间: 2012-01-25
课程语种: 英语
中文简介:
诸如机器学习和大型网络上的动态优化等领域中的问题导致极大的凸优化问题,问题数据以分散的方式存储,并且处理元件分布在网络上。我们认为乘法器的交替方向方法非常适合这些问题。该方法是在20世纪70年代发展起来的,源于20世纪50年代,与许多其他算法等效或密切相关,如双重分解,乘数法,道格拉斯拉赫福德分裂,Spingarn的部分逆的方法,Dykstra的交替投影,Bregman问题,近端方法等的迭代算法。在简要地研究了算法的理论和历史之后,我们讨论了应用于统计和机器学习问题,例如套索和支持向量机。
课程简介: Problems in areas such as machine learning and dynamic optimization on a large network lead to extremely large convex optimization problems, with problem data stored in a decentralized way, and processing elements distributed across a network. We argue that the alternating direction method of multipliers is well suited to such problems. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas-Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to statistical and machine learning problems such as the lasso and support vector machines.
关 键 词: 机器学习; 网络; 乘法器
课程来源: 视频讲座网
最后编审: 2020-06-01:王勇彬(课程编辑志愿者)
阅读次数: 313