A Riemannian under-determined BFGS method for least squares inverse eigenvalue problems

This paper is concerned with the parameterized least squares inverse eigenvalue problems for the case that the number of parameters to be constructed is greater than the number of prescribed realizable eigenvalues. Intrinsically, this is a specific problem of finding a zero of an under-determined no...

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Veröffentlicht in:	BIT 2022-03, Vol.62 (1), p.311-337
Hauptverfasser:	Zhao, Zhi, Jin, Xiao-Qing, Yao, Teng-Teng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computational Mathematics and Numerical Analysis Eigenvalues Iterative methods Least squares Linear operators Mathematics Mathematics and Statistics Newton methods Numeric Computing Operators (mathematics)
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