Randomized Algorithms for Stochastic Approximation under Arbitrary Disturbances

New algorithms for stochastic approximation under input disturbance are designed. For the multidimensional case, they are simple in form, generate consistent estimates for unknown parameters under almost arbitrary disturbances, and are easily incorporated in the design of quantum devices for estimating the gradient vector of a function of several variables.