Recurrent estimation of minimal eigenvalues of information matrices

A recurrent way of estimating the minimal eigenvalues of information matrices from observed data is developed. The proposed estimation algorithm is proved to converge globally almost surely. For parameter identification and stochastic adaptive control this approach leads to realizable asymptotically optimal stochastic approximation algorithms with a scalar gain matrix.