Stochastic approximation with long range dependent and heavy tailed noise
Stability and convergence properties of stochastic approximation algorithms are analyzed when the noise includes a long range dependent component (modeled by a fractional Brownian motion) and a heavy tailed component (modeled by a symmetric stable process), in addition to the usual ‘martingale noise...
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| Veröffentlicht in: | Queueing systems 2012-06, Vol.71 (1-2), p.221-242 |
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| container_title | Queueing systems |
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| creator | Anantharam, V. Borkar, V. S. |
| description | Stability and convergence properties of stochastic approximation algorithms are analyzed when the noise includes a long range dependent component (modeled by a fractional Brownian motion) and a heavy tailed component (modeled by a symmetric stable process), in addition to the usual ‘martingale noise’. This is motivated by the emergent applications in communications. The proofs are based on comparing suitably interpolated iterates with a limiting ordinary differential equation. Related issues such as asynchronous implementations, Markov noise, etc. are briefly discussed. |
| doi_str_mv | 10.1007/s11134-012-9283-0 |
| format | Article |
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| ispartof | Queueing systems, 2012-06, Vol.71 (1-2), p.221-242 |
| issn | 0257-0130 1572-9443 |
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| subjects | Algorithms Approximation Business and Management Computer Communication Networks Control Differential equations Electrical engineering Noise Operations Research/Decision Theory Ordinary differential equations Probability Theory and Stochastic Processes Queuing Robotics Studies Supply Chain Management Systems Theory |
| title | Stochastic approximation with long range dependent and heavy tailed noise |
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