On a class of signals and matrices giving very fast algorithms for linear problems

In this paper is presented a class of stochastic signals and of correlation matrices introducing very fast algorithms for solving linear problems. These signals are derived from white noise by using three kinds of operations combined in various orders: summation, difference and instantaneous modulation. The discrete time Brownian motion is a particular example of such signals.