Randomized stochastic approximation algorithms

Multidimensional stochastic optimization plays an important role in analysis and control of many technical systems. To solve the challenging problems of multidimensional optimization, it was suggested to use the randomized algorithms of stochastic approximation with perturbed input which have simple forms and provide consistent estimates of the unknown parameters for observations under “almost arbitrary” noise. They are easily “incorporated” in the design of quantum devices to estimate gradient vector of a multi-variable function.