Gradient procedures for stochastic approximation with dependent noise and their asymptotic behaviour

DOI10.1080/00207728508926724zbMath0572.62067OpenAlexW2083796681MaRDI QIDQ3690039

D. O. Tchickin, Alexander S. Poznyak

Publication date: 1985

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207728508926724

zbMATH Keywords

rate of convergenceasymptotic normalityalmost sure convergencemean-square convergencedependent noisegradient proceduresgeneralized strong convexity conditions

Mathematics Subject Classification ID

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

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Cites Work

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• Stochastic approximation with dependent noise
• Stochastic approximation methods for constrained and unconstrained systems
• A Stochastic Approximation Procedure in the Case of Weakly Dependent Observations
• Stochastic approximation with correlated data
• Limit Theorems and the Law of Large Numbers for Martingale-like Sequences
• Analysis of recursive stochastic algorithms
• Invariance principles for dependent variables
• Convergence of Distributions Generated by Stationary Stochastic Processes
• Asymptotic Distribution of Stochastic Approximation Procedures
• On Stochastic Approximation for Random Processes with Continuous Time

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