Convergence Rates of Digital Diffusion Network Algorithms for Global Optimization with Applications to Image Estimation |
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Authors: | G Yin PA Kelly |
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Institution: | (1) Department of Mathematics, Wayne State University, Detroit, MI 48202, USA;(2) Department of Electrical & Computer Engineering, University of Massachusetts, Amherst, MA 10013, USA |
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Abstract: | Motivated by the recent developments in digital diffusion networks, this work is devoted to the rates of convergence issue for a class of global optimization algorithms. By means of weak convergence methods, we show that a sequence of suitably scaled estimation errors converges weakly to a diffusion process (a solution of a stochastic differential equation). The scaling together with the stationary covariance of the limit diffusion process gives the desired rates of convergence. Application examples are also provided for some image estimation problems. |
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Keywords: | Global optimization Recursive algorithm Rate of convergence Image estimation |
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