STABILITY AND CONVERGENCE OF LARGE-SCALE STOCHASTIC-APPROXIMATION PROCEDURES

In practice, knowledge about input-output systems from various scienti fic fields such as engineering, biology and the social sciences is lim ited to output data. In this work we consider the continuous version o f the discrete approximation procedure relative to such a system whose outcome is known at a time and a point. Convergence and stability res ults of the solution of continuous time approximation procedures are d eveloped utilizing comparison principles in the context of vector Lyap unov-like functions. In particular, by considering continuous time app roximation procedures as large-scale procedures, similar results are o btained using the decomposition-aggregation principle.