Newton-based stochastic extremum seeking

Abstract

While the stochastic extremum seeking algorithms presented in all of the book’s earlier chapters are based on the gradient approach, this chapter introduces a Newton-based approach to stochastic extremum seeking. The advantage of the Newton approach is that, while the convergence of the gradient algorithm is dictated by the second derivative (Hessian matrix) of the map, which is unknown, rendering the convergence rate unknown to the user, the convergence of the Newton algorithm is proved to be independent of the Hessian matrix and can be arbitrarily assigned.