  The convergence rate of stochastic gradient search is analyzed in this paper.
Using arguments based on differential geometry and Lojasiewicz inequalities,
tight bounds on the convergence rate of general stochastic gradient algorithms
are derived. As opposed to the existing results, the results presented in this
paper allow the objective function to have multiple, non-isolated minima,
impose no restriction on the values of the Hessian (of the objective function)
and do not require the algorithm estimates to have a single limit point.
Applying these new results, the convergence rate of recursive prediction error
identification algorithms is studied. The convergence rate of supervised and
temporal-difference learning algorithms is also analyzed using the results
derived in the paper.
