  We develop a stochastic approximation version of the classical Kaczmarz
algorithm that is incremental in nature and takes as input noisy real time
data. Our analysis shows that with probability one it mimics the behavior of
the original scheme: starting from the same initial point, our algorithm and
the corresponding deterministic Kaczmarz algorithm converge to precisely the
same point. The motivation for this work comes from network tomography where
network parameters are to be estimated based upon end-to-end measurements.
Numerical examples via Matlab based simulations demonstrate the efficacy of the
algorithm.
