Stochastic approximation search algorithms with randomization at the input

This work presents a comprehensive survey of the development of pseudogradient stochastic approximation algorithms with randomized input disturbance, considers the problems of their applicability in optimization problems with linear constraints, and discusses new possibilities to use them for multiagent control for load balancing of nodes in computational networks. Justifications of the algorithms’ correctness and their optimal convergence rate are based on the foundational works of B.T. Polyak.