Open AccessMatrix LSQR algorithms for solving constrained quadratic inverse eigenvalue problem
Author(s) -
Masoud Hajarian
Publication year - 2021
Publication title -
filomat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 34
eISSN - 2406-0933
pISSN - 0354-5180
DOI - 10.2298/fil2109105h
Subject(s) - eigenvalues and eigenvectors , mathematics , inverse , inverse problem , constraint (computer aided design) , quadratic equation , matrix (chemical analysis) , mathematical optimization , algebra over a field , algorithm , mathematical analysis , pure mathematics , geometry , physics , materials science , quantum mechanics , composite material
The inverse eigenvalue problem appears in many applications such as control design, seismic tomography, exploration and remote sensing, molecular spectroscopy, particle physics, structural analysis, and mechanical system simulation. This paper investigates the matrix form of LSQR methods for solving the quadratic inverse eigenvalue problem with partially bisymmetric matrices under a prescribed submatrix constraint. In order to illustrate the effectiveness and feasibility of our results, one numerical example is presented.