Volume 10, Issue 2
Algorithms for Inverse Eigenvalue Problems

Ren-cang Li

DOI:

J. Comp. Math., 10 (1992), pp. 97-111

Published online: 1992-10

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  • Abstract

Two new algorithms based on QR decompositions (QRDs)(with column pivoting) are proposed for solving inverse eigenvalue problems, and under some non-singularity assumptions they are both locally quadratically convergent. Several numerical tests are presented to illustrate their convergence behaveior.

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@Article{JCM-10-97, author = {}, title = {Algorithms for Inverse Eigenvalue Problems}, journal = {Journal of Computational Mathematics}, year = {1992}, volume = {10}, number = {2}, pages = {97--111}, abstract = { Two new algorithms based on QR decompositions (QRDs)(with column pivoting) are proposed for solving inverse eigenvalue problems, and under some non-singularity assumptions they are both locally quadratically convergent. Several numerical tests are presented to illustrate their convergence behaveior. }, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/9344.html} }
TY - JOUR T1 - Algorithms for Inverse Eigenvalue Problems JO - Journal of Computational Mathematics VL - 2 SP - 97 EP - 111 PY - 1992 DA - 1992/10 SN - 10 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/9344.html KW - AB - Two new algorithms based on QR decompositions (QRDs)(with column pivoting) are proposed for solving inverse eigenvalue problems, and under some non-singularity assumptions they are both locally quadratically convergent. Several numerical tests are presented to illustrate their convergence behaveior.
Ren-cang Li. (1970). Algorithms for Inverse Eigenvalue Problems. Journal of Computational Mathematics. 10 (2). 97-111. doi:
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