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Proceedings Paper

Some fast Toeplitz least-squares algorithms
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Paper Abstract

We study fast preconditioned conjugate gradient (PCG) methods for solving least squares problems min (parallel)b-T(chi) (parallel)2, where T is an m X n Toeplitz matrix of rank n. Two circulant preconditioners are suggested: one, denoted by P, is based on a block partitioning of T and the other, denoted by N, is based on the displacement representation of TTT. Each is obtained without forming TTT. We prove formally that for a wide class of problems the PCG method with P converges in a small number of iterations independent of m and n, so that the computational cost of solving such Toeplitz least squares problems is O(m log n). Numerical experiments in using both P and N are reported, indicating similar good convergence properties for each preconditioner.

Paper Details

Date Published: 1 December 1991
PDF: 12 pages
Proc. SPIE 1566, Advanced Signal Processing Algorithms, Architectures, and Implementations II, (1 December 1991); doi: 10.1117/12.49810
Show Author Affiliations
James G. Nagy, North Carolina State Univ. (United States)
Robert J. Plemmons, Wake Forest Univ. (United States)

Published in SPIE Proceedings Vol. 1566:
Advanced Signal Processing Algorithms, Architectures, and Implementations II
Franklin T. Luk, Editor(s)

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