| Title | : | Simultaneous Perturbation Methods for Stochastic Non-convex Optimization - I |
| Speaker | : | L A Prashanth (IITM) |
| Details | : | Tue, 24 Oct, 2017 3:00 PM @ Ada Lovelace Confere |
| Abstract: | : | Stochastic recursive algorithms form the basis of several optimization approaches and find wide applicability across various engineering disciplines such as machine learning, communication engineering, signal processing and robotics. The highly efficient simultaneous perturbation approaches have been considered as the unifying thread in all these algorithms. This tutorial presents algorithms for smooth non-convex optimization based on the simultaneous perturbation method. We may mention here that in this proposal, by simultaneous perturbation methods, we refer to the entire family of algorithms that are based on either gradient or gradient and Hessian estimates that are obtained using some form of simultaneous random perturbations. A remarkable feature of the algorithms is that they are easily implementable, do not require an explicit system model, and work with real or simulated data. The tutorial also covers applications in sensor networks and service systems to illustrate these points. |
