Author: Yin G. Kelly P.A. Dowell M.H.
Publisher: Springer Publishing Company
ISSN: 0022-3239
Source: Journal of Optimization Theory and Applications, Vol.107, Iss.2, 2000-11, pp. : 391-414
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Abstract
This work is concerned with a numerical procedure for approximating an analog diffusion network. The key idea is to take advantage of the separable feature of the noise for the diffusion machine and use a parallel processing method to develop recursive algorithms. The asymptotic properties are studied. The main result of this paper is to establish the convergence of a continuous-time interpolation of the discrete-time algorithm to that of the analog diffusion network via weak convergence methods. The parallel processing feature of the network makes it attractive for solving large-scale optimization problems. Applications to image estimation are considered. Not only is this algorithm useful for the image estimation problems, but it is widely applicable to many related optimization problems.
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