Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient

DOI10.1080/02331888.2023.2213371arXiv2107.12058OpenAlexW4287067670MaRDI QIDQ6168293

Publication date: 10 July 2023

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.12058

zbMATH Keywords

stochastic optimization online learning averaging stochastic gradient algorithm non-asymptotic convergence

Mathematics Subject Classification ID

Stochastic approximation (62L20) Sequential estimation (62L12)

Cites Work

Unnamed Item
Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm
Estimating the geometric median in Hilbert spaces with stochastic gradient algorithms: \(L^p\) and almost sure rates of convergence
Multivariate location estimation using extension of \(R\)-estimates through \(U\)-statistics type approach
On the almost sure asymptotic behaviour of stochastic algorithm
Online estimation of the geometric median in Hilbert spaces: nonasymptotic confidence balls
Non asymptotic controls on a recursive superquantile approximation
Online estimation of the asymptotic variance for averaged stochastic gradient algorithms
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
A Generalization of the Averaging Procedure: The Use of Two-Time-Scale Algorithms
Acceleration of Stochastic Approximation by Averaging
Asymptotic Almost Sure Efficiency of Averaged Stochastic Algorithms
On Projected Stochastic Gradient Descent Algorithm with Weighted Averaging for Least Squares Regression
A Stochastic Approximation Method
L^p and almost sure rates of convergence of averaged stochastic gradient algorithms: locally strongly convex objective

This page was built for publication: Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient