Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize - École polytechnique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

Alexey Naumov
  • Fonction : Auteur
Sergey Samsonov
  • Fonction : Auteur
Kevin Scaman
  • Fonction : Auteur
Hoi-To Wai
  • Fonction : Auteur

Résumé

This paper provides a non-asymptotic analysis of linear stochastic approximation (LSA) algorithms with fixed stepsize. This family of methods arises in many machine learning tasks and is used to obtain approximate solutions of a linear system $\bar{A}\theta = \bar{b}$ for which $\bar{A}$ and $\bar{b}$ can only be accessed through random estimates $\{({\bf A}_n, {\bf b}_n): n \in \mathbb{N}^*\}$. Our analysis is based on new results regarding moments and high probability bounds for products of matrices which are shown to be tight. We derive high probability bounds on the performance of LSA under weaker conditions on the sequence $\{({\bf A}_n, {\bf b}_n): n \in \mathbb{N}^*\}$ than previous works. However, in contrast, we establish polynomial concentration bounds with order depending on the stepsize. We show that our conclusions cannot be improved without additional assumptions on the sequence of random matrices $\{{\bf A}_n: n \in \mathbb{N}^*\}$, and in particular that no Gaussian or exponential high probability bounds can hold. Finally, we pay a particular attention to establishing bounds with sharp order with respect to the number of iterations and the stepsize and whose leading terms contain the covariance matrices appearing in the central limit theorems.
Fichier principal
Vignette du fichier
2106.01257v1.pdf (367.8 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04399029 , version 1 (12-07-2024)

Identifiants

Citer

Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, et al.. Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. Advances in Neural Information Processing Systems, Dec 2021, Online, France. pp.30063 - 30074, ⟨10.5555/3540261.3542562⟩. ⟨hal-04399029⟩
9 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More