CONVERGENCE THEOREMS FOR A CLASS OF SIMULATED ANNEALING ALGORITHMS ON R(D)

We study a class of simulated annealing algorithms for global minimization of a continuous function defined on a subset of R(d). We consider the case where the selection Markov kernel is absolutely continuous and has a density which is uniformly bounded away from 0. This class includes certain simulated annealing algorithms recently introduced by various authors. We show that, under mild conditions, the sequence of states generated by these algorithms converges in probability to the global minimum of the function. Unlike most previous studies where the cooling schedule is deterministic, our cooling schedule is allowed to be adaptive. We also address the issue of almost sure convergence versus convergence in probability.