Magdon-Ismail, Malik and Atiya, Amir F. (2000) The Early Restart Algorithm. Neural Computation, 12 (6). pp. 1303-1312. ISSN 0899-7667. http://resolver.caltech.edu/CaltechAUTHORS:20111128-151723698
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Consider an algorithm whose time to convergence is unknown (because of some random element in the algorithm, such as a random initial weight choice for neural network training). Consider the following strategy. Run the algorithm for a specific time T. If it has not converged by time T, cut the run short and rerun it from the start (repeat the same strategy for every run). This so-called restart mechanism has been proposed by Fahlman (1988) in the context of backpropagation training. It is advantageous in problems that are prone to local minima or when there is a large variability in convergence time from run to run, and may lead to a speed-up in such cases. In this article, we analyze theoretically the restart mechanism, and obtain conditions on the probability density of the convergence time for which restart will improve the expected convergence time. We also derive the optimal restart time. We apply the derived formulas to several cases, including steepest-descent algorithms.
|Additional Information:||© 2000 Massachusetts Institute of Technology. Received October 2, 1998; accepted April 13, 1999. Posted Online March 13, 2006. We thank Yaser Abu-Mostafa and the Caltech Learning Systems Group for their useful input. We also acknowledge the support of NSF’s Engineering Research Center at Caltech.|
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|Deposited By:||Tony Diaz|
|Deposited On:||29 Nov 2011 17:38|
|Last Modified:||26 Dec 2012 14:27|
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