| 来源类型 | Monograph (IIASA Working Paper) |
| 规范类型 | 论文 |
| Linear Convergence of Epsilon-Subgradient Descent Methods for a Class of Convex Functions. | |
| Robinson SM | |
| 发表日期 | 1996 |
| 出版者 | IIASA, Laxenburg, Austria: WP-96-041 |
| 出版年 | 1996 |
| 语种 | 英语 |
| 摘要 | This paper establishes a linear convergence rate for a class of epsilon-subgradient descent methods for minimizing certain convex functions. Currently prominent methods belonging to this class include the resolvent (proximal point) method and the bundle method in proximal form (considered as a sequence of serious steps). Other methods, such as the recently proposed descent proximal level method, may also fit this framework depending on implementation. The convex functions covered by the analysis are those whose conjugates have subdifferentials that are locally upper Lipschitzian at the origin, a class introduced by Zhang and Treiman. We argue that this class is a natural candidate for study in connection with minimization algorithms. |
| 主题 | Optimization under Uncertainty (OPT) |
| URL | http://pure.iiasa.ac.at/id/eprint/4985/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/124616 |
| 推荐引用方式 GB/T 7714 | Robinson SM. Linear Convergence of Epsilon-Subgradient Descent Methods for a Class of Convex Functions.. 1996. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| WP-96-041.pdf（208KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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