Multi-Time Scale Smoothed Functional With Nesterov’s Acceleration

Multi-Time Scale Smoothed Functional With Nesterov’s Acceleration

Smoothed functional (SF) algorithm estimates the gradient of the stochastic optimization problem by convolution with a smoothening kernel. This process helps the algorithm to converge to a global minimum or a point close to it. We study a two-time scale SF based gradient search algorithm with Nester...

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Bibliographic Details
Main Authors: Abhinav Sharma, K. Lakshmanan, Ruchir Gupta, Atul Gupta
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Multi-Stage queueing networks
Nesterov’s acceleration
simulation
smoothed functional algorithm
stochastic approximation algorithms
stochastic optimization
Online Access:https://ieeexplore.ieee.org/document/9509526/

Internet

https://ieeexplore.ieee.org/document/9509526/

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