The majority of stochastic optimization algorithms can be written in the general form xt+1 = Tt(xt; yt), where xt is a sequence of points and parameters which are transformed by the algorithm, Tt are the methods of the algorithm and yt represent the randomness of the algorithm. We extend the results of papers [11] and [14] to provide some new general conditions under which the algorithm finds a global minimum with probability one.
