Bhatnagar, S. ; Prasad, H.L. ; Prashanth, L.A. (2013) Stochastic Recursive Algorithms for Optimization Lecture Notes in Control and Information Sciences Series, 434 (1). Springer Nature. ISBN 978-1-4471-4284-3
Full text not available from this repository.
Official URL: https://doi.org/10.1007/978-1-4471-4285-0
Related URL: http://dx.doi.org/10.1007/978-1-4471-4285-0
Abstract
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.
| Item Type: | Book |
|---|---|
| Source: | Copyright of this article belongs to Springer Nature. |
| Keywords: | Gradient Estimation; Hessian Estimation; Optimization Techniques; Simultaneous Perturbation Methods; Stochastic Algorithms. |
| ID Code: | 116410 |
| Deposited On: | 12 Apr 2021 05:43 |
| Last Modified: | 12 Apr 2021 05:43 |
Repository Staff Only: item control page
