Inhaltsangabe
I Foundations of stochastic approximation.- §1 Almost sure convergence of stochastic approximation procedures.- §2 Recursive methods for linear problems.- §3 Stochastic optimization under stochastic constraints.- §4 A learning model; recursive density estimation.- §5 Invariance principles in stochastic approximation.- §6 On the theory of large deviations.- References for Part I.- II Applicational aspects of stochastic approximation.- §7 Markovian stochastic optimization and stochastic approximation procedures.- §8 Asymptotic distributions.- §9 Stopping times.- §10 Applications of stochastic approximation methods.- References for Part II.- III Applications to adaptation algorithms.- §11 Adaptation and tracking.- §12 Algorithm development.- §13 Asymptotic Properties in the decreasing gain case.- §14 Estimation of the tracking ability of the algorithms.- References for Part III.
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