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Sparse recovery by reduced variance stochastic approximation

Anatoli Juditsky 1 Andrei Kulunchakov 2 Hlib Tsyntseus 1
1 DAO - Données, Apprentissage et Optimisation
LJK - Laboratoire Jean Kuntzmann
2 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : In this paper, we discuss application of iterative Stochastic Optimization routines to the problem of sparse signal recovery from noisy observation. Using Stochastic Mirror Descent algorithm as a building block, we develop a multistage procedure for recovery of sparse solutions to Stochastic Optimization problem under assumption of smoothness and quadratic minoration on the expected objective. An interesting feature of the proposed algorithm is its linear convergence of the approximate solution during the preliminary phase of the routine when the component of stochastic error in the gradient observation which is due to bad initial approximation of the optimal solution is larger than the "ideal" asymptotic error component owing to observation noise "at the optimal solution." We also show how one can straightforwardly enhance reliability of the corresponding solution by using Median-of-Means like techniques. We illustrate the performance of the proposed algorithms in application to classical problems of recovery of sparse and low rank signals in linear regression framework. We show, under rather weak assumption on the regressor and noise distributions, how they lead to parameter estimates which obey (up to factors which are logarithmic in problem dimension and confidence level) the best known to us accuracy bounds.
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Preprints, Working Papers, ...
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Contributor : Anatoli Juditsky Connect in order to contact the contributor
Submitted on : Tuesday, March 30, 2021 - 1:36:08 PM
Last modification on : Friday, February 4, 2022 - 3:30:03 AM

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  • HAL Id : hal-03185516, version 1
  • ARXIV : 2006.06365



Anatoli Juditsky, Andrei Kulunchakov, Hlib Tsyntseus. Sparse recovery by reduced variance stochastic approximation. 2021. ⟨hal-03185516⟩



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