Linear and Nonlinear Deconvolution Models
Olkin, Julia Ann
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/16001
This dissertation considers computational methods for solving linear and nonlinear least squares problems arising from deconvolution applications. For the linear problems we propose a new preconditioner to speed up the conjugate gradient algorithm. This preconditioner is based on Cybenko's QR factorization of a circulant matrix. Several cases are presented in which our method reduces the amount of computation.
Citable link to this pagehttps://hdl.handle.net/1911/101598
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