Optimization methods are of a great practical importance in systems analysis. They allow us to find the best behavior of a system, determine the optimal structure and compute the optimal parameters of the control system etc. The development of nondifferentiable optimization, differentiable and nondifferentiable stochastic optimization allows us to state and effectively solve new complex optimization problems which were impossible to solve by classical optimization methods.
The main purpose of this article is to review briefly some important applications of nondifferentiable and stochastic optimization and to characterize principal directions of research. Clearly, the interests of the author have influenced the content of this article
