An alternative proof for convergence of stochastic approximation algorithms

An alternative proof for convergence of stochastic approximation algorithms is provided. The proof is completely deterministic, very elementary (involving only basic notions of convergence), and direct in that it remains in a discrete setting. An alternative form of the Kushner-Clark condition is introduced and utilized and the results are the first to prove necessity for general gain sequences in a Hilbert space setting.