Convergence and Optimality of Realizable Adaptation Algorithms (Information-Theory Approach)

Realizable variants of optimal and robust Tsypkin–Polyak stochastic optimization procedures are investigated. A Liapunov function of information type is used to prove convergence of realizable algorithms. It is shown that realizable procedures possess asymptotically optimal properties.