Sastry, PS and Magesh, M and Unnikrishnan, KP (2002) Two timescale analysis of the Alopex algorithm for optimization. In: Neural Computation, 14 (11). pp. 2729-2750.
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Alopex is a correlation-based gradient-free optimization technique useful in many learning problems. However, there are no analytical results on the asymptotic behavior of this algorithm. This article presents a new version of Alopex that can be analyzed using techniques of two timescale stochastic approximation method. It is shown that the algorithm asymptotically behaves like a gradient-descent method, though it does not need (or estimate) any gradient information. It is also shown, through simulations, that the algorithm is quite effective.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to MIT Press.|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||28 Jul 2011 04:32|
|Last Modified:||28 Jul 2011 04:32|
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