The University of Southampton
University of Southampton Institutional Repository

On the computation of the structured total least squares estimator

On the computation of the structured total least squares estimator
On the computation of the structured total least squares estimator
A class of structured total least squares problems is considered, in which the extended data matrix is partitioned into blocks and each of the blocks is (block) Toeplitz/Hankel structured, unstructured, or noise free. We describe the implementation of two types of numerical solution methods for this problem: i) standard local optimization methods in combination with efficient evaluation of the cost function and its gradient, and ii) an iterative procedure proposed originally for the element-wise weighted total least squares problem. The computational efficiency of the proposed methods is compared with this of alternative methods. Application of the structured total least squares problem for system identification and model reduction is described and illustrated with numerical examples.
parameter estimation, total least squares, structured total least squares, system identification, Hankel low rank approximation, model reduction.
591-608,
Markovsky, I.
3e68743b-f22e-4b2b-b1a8-2ba4eb036a69
Van Huffel, S.
e64be3d0-00e1-4900-ab8e-74aed4792678
Kukush, A.
9cf76e13-c463-47c3-9467-0ae6b04df4ef
Markovsky, I.
3e68743b-f22e-4b2b-b1a8-2ba4eb036a69
Van Huffel, S.
e64be3d0-00e1-4900-ab8e-74aed4792678
Kukush, A.
9cf76e13-c463-47c3-9467-0ae6b04df4ef

Markovsky, I., Van Huffel, S. and Kukush, A. (2004) On the computation of the structured total least squares estimator. Numerical Linear Algebra with Applications, 11, 591-608,.

Record type: Article

Abstract

A class of structured total least squares problems is considered, in which the extended data matrix is partitioned into blocks and each of the blocks is (block) Toeplitz/Hankel structured, unstructured, or noise free. We describe the implementation of two types of numerical solution methods for this problem: i) standard local optimization methods in combination with efficient evaluation of the cost function and its gradient, and ii) an iterative procedure proposed originally for the element-wise weighted total least squares problem. The computational efficiency of the proposed methods is compared with this of alternative methods. Application of the structured total least squares problem for system identification and model reduction is described and illustrated with numerical examples.

Text
stls_comp_published.pdf - Other
Download (158kB)

More information

Published date: 2004
Keywords: parameter estimation, total least squares, structured total least squares, system identification, Hankel low rank approximation, model reduction.
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263297
URI: https://eprints.soton.ac.uk/id/eprint/263297
PURE UUID: 218bc03c-022a-4ae6-9fe0-5d402da2d6b8

Catalogue record

Date deposited: 06 Jan 2007
Last modified: 28 Aug 2019 19:00

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×