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Approximation of an Analog Diffusion Network with Applications to Image Estimation

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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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Authors and Affiliations

  1. Department of Mathematics, Wayne State University, Detroit, Michigan

    G. Yin (Professor)

  2. Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts

    P. A. Kelly (Associate Professor)

  3. PrairieComm, Rolling Meadows, Illinois

    M. H. Dowll (Associate Engineer)

Authors
  1. G. Yin
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  2. P. A. Kelly
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  3. M. H. Dowll
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Yin, G., Kelly, P.A. & Dowll, M.H. Approximation of an Analog Diffusion Network with Applications to Image Estimation. Journal of Optimization Theory and Applications 107, 391–414 (2000). https://doi.org/10.1023/A:1026485504384

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  • DOI: https://doi.org/10.1023/A:1026485504384

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