| 书目名称 | Stochastic Recursive Algorithms for Optimization |
| 副标题 | Simultaneous Perturb |
| 编辑 | S. Bhatnagar,H.L. Prasad,L.A. Prashanth |
| 视频video | |
| 概述 | Algorithms described perform better in real-life settings than many previously described in the literature.Detailed mathematical treatment of the algorithms proposed is provided using both gradient- a |
| 丛书名称 | Lecture Notes in Control and Information Sciences |
| 图书封面 |  |
| 描述 | Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: .• are easily implemented; .• do not require an explicit system model; and .• work with real or simulated data. .Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. .The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations re |
| 出版日期 | Book 2013 |
| 关键词 | Gradient Estimation; Hessian Estimation; Optimization Techniques; Simultaneous Perturbation Methods; Sto |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4471-4285-0 |
| isbn_softcover | 978-1-4471-4284-3 |
| isbn_ebook | 978-1-4471-4285-0Series ISSN 0170-8643 Series E-ISSN 1610-7411 |
| issn_series | 0170-8643 |
| copyright | Springer-Verlag London 2013 |
发表于 2025-3-21 18:24:14
