Optimal pseudogradient adaptation algorithms

The asymptotic rate of convergence of pseudogradient algorithms is investigated with a view to finding the unconditional extremum of the function in the presence of random noises in computing its gradient. Optimal pseudogradient algorithms are found which insure the maximal rate. Optimal pseudogradient algorithms require nonlinear transformation of the gradient; the form of that transformation is dictated by the noise distribution law.