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Proceedings Paper

Performance Analysis Of Matrix Preconditioning Algorithms On Parallel Optical Processors.
Author(s): Anjan Ghosh
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Paper Abstract

An efficient way of using analog optical associative processors is to implement robust computational algorithms that require high throughput rate but exhibit tolerance for roundoff errors and noise. Matrix preconditioning algorithms used for preprocessing the data of linear algebraic equations have these properties. In this paper, the performance of polynomial matrix preconditioning algorithms on optical processors is analyzed. The results of the error analysis and numerical experiments show that for a given set of data the spatial errors and detector noise below a certain threshold level do not affect the accuracy of optical preconditioning. It is also shown that optical preconditioning improves the rate of convergence and the accuracy of the final solution of a linear algebra problem. Simple and efficient optical preprocessors designed with preconditioning algorithms can thus assist parallel solvers of linear algebraic equations and other engineering problems.

Paper Details

Date Published: 18 July 1988
PDF: 10 pages
Proc. SPIE 0939, Hybrid Image and Signal Processing, (18 July 1988); doi: 10.1117/12.947042
Show Author Affiliations
Anjan Ghosh, The University of Iowa (United States)


Published in SPIE Proceedings Vol. 0939:
Hybrid Image and Signal Processing
David P. Casasent; Andrew G. Tescher, Editor(s)

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