  A stochastic iterative algorithm approximating second-order information using
von Neumann series is discussed. We present convergence guarantees for
strongly-convex and smooth functions. Our analysis is much simpler in contrast
to a similar algorithm and its analysis, LISSA. The algorithm is primarily
suitable for training large scale linear models, where the number of data
points is very large. Two novel analyses, one showing space independent linear
convergence, and one showing conditional quadratic convergence are discussed.
In numerical experiments, the behavior of the error is similar to the
second-order algorithm L-BFGS, and improves the performance of LISSA for
quadratic objective function.
