Mathematics – Optimization and Control
Scientific paper
2009-04-27
Mathematics
Optimization and Control
Scientific paper
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.
No associations
LandOfFree
Convergence Rate of Stochastic Gradient Search in the Case of Multiple and Non-Isolated Minima does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Convergence Rate of Stochastic Gradient Search in the Case of Multiple and Non-Isolated Minima, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Convergence Rate of Stochastic Gradient Search in the Case of Multiple and Non-Isolated Minima will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-323300