Stochastic approximation with long range dependent and heavy tailed noise

DOI10.1007/s11134-012-9283-0zbMath1275.60053OpenAlexW2019544144MaRDI QIDQ383264

Venkat Anantharam, Vivek S. Borkar

Publication date: 25 November 2013

Published in: Queueing Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11134-012-9283-0

zbMATH Keywords

stochastic approximationlong range dependenceconvergence in \(\xi\)th meanheavy tailed noiseO.d.e. limit

Mathematics Subject Classification ID

Applications of stochastic analysis (to PDEs, etc.) (60H30) Stochastic approximation (62L20) Stochastic learning and adaptive control (93E35)

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Cites Work

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• On maximal inequalities for stable stochastic integrals
• Asymptotic stability equals exponential stability, and ISS equals finite energy gain -- if you twist your eyes.
• Is network traffic approximated by stable Lévy motion or fractional Brownian motion?
• The Impact of Stochastic Noisy Feedback on Distributed Network Utility Maximization
• Some Examples of Stochastic Approximation in Communications
• Stochastic Approximation for Nonexpansive Maps: Application to Q-Learning Algorithms
• A Dynamical System Approach to Stochastic Approximations
• The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
• Dynamic Cesaro-Wardrop equilibration in networks
• Smoothing Derivatives of Functions and Applications