Weighted averaging and stochastic approximation
نویسندگان
چکیده
منابع مشابه
Weighted averaging and stochastic approximation
We explore the relationship between weighted averaging and stochastic approxima tion algorithms and study their convergence via a sample path analysis We prove that the convergence of a stochastic approximation algorithm is equivalent to the con vergence of the weighted average of the associated noise sequence We also present necessary and su cient noise conditions for convergence of the averag...
متن کاملStochastic Approximation with Averaging Innovation
The aim of the paper is to establish a convergence theorem for multi-dimensional stochastic approximation in a setting with innovations satisfying some averaging properties and to study some applications. The averaging assumptions allow us to unify the framework where the innovations are generated (to solve problems from Numerical Probability) and the one with exogenous innovations (market data...
متن کاملWeighted Averaging and Stochastic Approximation - Decision and Control, 1996., Proceedings of the 35th IEEE
We explore the relationship between weighted averaging and stochastic approximation algorithms, and study their convergence via a sample-path analysis. We prove that the convergence of a stochastic approximation algorithm is equivalent to the convergence of the weighted average of the associated noise sequence. We also present necessary and sufficient noise conditions for convergence of the ave...
متن کاملParallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging
This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). In particular, this work sharply analyzes: (1) mini-batching, a method of averaging many samples of the gradient to both reduce the variance of a stochastic gradient estimate and for parallelizing SGD and (2) tail-averaging, a method involving averaging the final few i...
متن کاملLinear Stochastic Approximation: Constant Step-Size and Iterate Averaging
We consider d-dimensional linear stochastic approximation algorithms (LSAs) with a constant step-size and the so called Polyak-Ruppert (PR) averaging of iterates. LSAs are widely applied in machine learning and reinforcement learning (RL), where the aim is to compute an appropriate θ∗ ∈ R (that is an optimum or a fixed point) using noisy data and O(d) updates per iteration. In this paper, we ar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematics of Control, Signals, and Systems
سال: 1997
ISSN: 0932-4194,1435-568X
DOI: 10.1007/bf01219775