Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences


UC Berkeley


2010 Research Summary

Stochastic Approximation in the presence of heavy-tailed and long-range-dependent noise

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Venkat Anantharam and Vivek Borkar1


The theory of stochastic approximation is widely used to develop algorithms convergent to global optima under noisy measurements. Typically the noise is modeled as a Brownian motion or other short range dependent process. In networks with long-range-dependent traffic, such noise models are inadequate. We are developing a theory of stochastic approximation that is broader in scope and can include heavy-tailed and long-range-dependent noise in the measurements.

1TIFR, Mumbai