On the rates of convergence of parallelized averaged stochastic gradient algorithms

The growing interest for high-dimensional and functional data analysis led in the last decade to important research developing a consequent amount of techniques. Parallelized algorithms, which consist of distributing and treat the data into different machines, for example, are a good answer to deal...

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Bibliographic Details
Published in:	Statistics (Berlin, DDR) Vol. 54; no. 3; pp. 618 - 635
Main Authors:	Godichon-Baggioni, Antoine, Saadane, Sofiane
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 03.05.2020 Taylor & Francis Ltd Taylor & Francis: STM, Behavioural Science and Public Health Titles
Subjects:	Algorithms asynchronous parallel optimization averaging Central limit theorem Convergence Data analysis Dimensional analysis distributed estimation Machinery Normality Parallel processing Statistics Stochastic gradient descent Distributed estimation Averaging Central Limit Theorem Stochastic Gradient Descent Asynchronous parallel optimization
ISSN:	0233-1888, 1029-4910
Online Access:	Get full text
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