  A stochastic conjugate gradient method for approximation of a function is
proposed. The proposed method avoids computing and storing the covariance
matrix in the normal equations for the least squares solution. In addition, the
method performs the conjugate gradient steps by using an inner product that is
based stochastic sampling. Theoretical analysis shows that the method is
convergent in probability. The method has applications in such fields as
predistortion for the linearization of power amplifiers.
