图 书 馆
凸优化非精确近端梯度算法的收敛速度Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization |
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| 课程网址: | http://videolectures.net/nips2011_schmidt_convex/ |
| 主讲教师: | Mark Schmidt |
| 开课单位: | 不列颠哥伦比亚大学 |
| 开课时间: | 2012-01-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal-gradient methods, where an error is present in the calculation of the gradient of the smooth term or in the proximity operator with respect to the second term. We show that the basic proximal-gradient method, the basic proximal-gradient method with a strong convexity assumption, and the accelerated proximal-gradient method achieve the same convergence rates as in the error-free case, provided the errors decrease at an appropriate rate. Our experimental results on a structured sparsity problem indicate that sequences of errors with these appealing theoretical properties can lead to practical performance improvements. |
| 关 键 词: | 近端梯度; 平滑凸函数; 凸性假设 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-01-17：chenxin |
| 阅读次数: | 99 |