Convergence of a global stochastic optimization algorithm with partial step size restarting

This work develops a class of stochastic global optimization algorithms that are Kiefer–Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.