  We propose a parallel stochastic Newton method (PSN) for minimizing
unconstrained smooth convex functions. We analyze the method in the strongly
convex case, and give conditions under which acceleration can be expected when
compared to its serial counterpart. We show how PSN can be applied to the
empirical risk minimization problem, and demonstrate the practical efficiency
of the method through numerical experiments and models of simple matrix
classes.
