Geodesic
Matrix LSQR algorithms for solving constrained quadratic inverse eigenvalue problem
Filomat, Tome 35 (2021) no. 9, p. 3105

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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
DOI : 10.2298/FIL2109105H
Classification : 15A24, 65H10, 15A69, 65F10
Keywords: Quadratic inverse eigenvalue problem, LSQR method, Partially bisymmetric matrix 
Masoud Hajarian. Matrix LSQR algorithms for solving constrained quadratic inverse eigenvalue problem. Filomat, Tome 35 (2021) no. 9, p. 3105 . doi: 10.2298/FIL2109105H
@article{10_2298_FIL2109105H,
     author = {Masoud Hajarian},
     title = {Matrix {LSQR} algorithms for solving constrained quadratic inverse eigenvalue problem},
     journal = {Filomat},
     pages = {3105 },
     year = {2021},
     volume = {35},
     number = {9},
     doi = {10.2298/FIL2109105H},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2109105H/}
}
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