| 书目名称 | Stochastic Approximation Methods for Constrained and Unconstrained Systems |
| 编辑 | Harold J. Kushner,Dean S. Clark |
| 视频video | |
| 丛书名称 | Applied Mathematical Sciences |
| 图书封面 |  |
| 描述 | The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to quali |
| 出版日期 | Book 1978 |
| 关键词 | Parameter; Power; Rang; Stochastische Approximation; probability |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4684-9352-8 |
| isbn_softcover | 978-0-387-90341-5 |
| isbn_ebook | 978-1-4684-9352-8Series ISSN 0066-5452 Series E-ISSN 2196-968X |
| issn_series | 0066-5452 |
| copyright | Springer Science+Business Media New York 1978 |
发表于 2025-3-21 19:18:41
