Please use this identifier to cite or link to this item: https://doi.org/10.1137/S0895479803434185
Title: The inverse eigenproblem of centrosymmetric matrices with a submatrix constraint and its approximation
Authors: Bai, Z.-J. 
Keywords: Best approximation
Centrosymmetric matrix
Inverse problem
Issue Date: 2005
Citation: Bai, Z.-J. (2005). The inverse eigenproblem of centrosymmetric matrices with a submatrix constraint and its approximation. SIAM Journal on Matrix Analysis and Applications 26 (4) : 1100-1114. ScholarBank@NUS Repository. https://doi.org/10.1137/S0895479803434185
Abstract: In this paper, we first consider the existence of and the general expression for the solution to the constrained inverse eigenproblem defined as follows: given a set of complex n-vectors {x i} i=1 m and a set of complex numbers {λ i} i=1 m and an s-by-s real matrix Co, find an n-by-n real centrosymmetric matrix C such that the s-by-s leading principal submatrix of C is C 0, and {x i} i=1 m and {λ i} i=1 m are the eigenvectors and eigenvalues of C, respectively. We are then concerned with the best approximation problem for the constrained inverse problem whose solution set is nonempty. That is, given an arbitrary real n-by-n matrix C̃, find a matrix C which is the solution to the constrained inverse problem such that the distance between C and C̃ is minimized in the Frobenius norm. We give an explicit solution and a numerical algorithm to the best approximation problem. Some illustrative experiments are also presented. © 2005 Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Matrix Analysis and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/130437
ISSN: 08954798
DOI: 10.1137/S0895479803434185
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

39
checked on Feb 19, 2019

WEB OF SCIENCETM
Citations

36
checked on Feb 11, 2019

Page view(s)

12
checked on Jan 11, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.