Download e-book for kindle: Matrix algorithms, vol.2.. Eigensystems by G. W. Stewart

By G. W. Stewart

ISBN-10: 0898714141

ISBN-13: 9780898714142

ISBN-10: 0898714184

ISBN-13: 9780898714180

ISBN-10: 0898715032

ISBN-13: 9780898715033

This can be the second one quantity in a projected five-volume survey of numerical linear algebra and matrix algorithms. It treats the numerical answer of dense and large-scale eigenvalue issues of an emphasis on algorithms and the theoretical history required to appreciate them. The notes and reference sections include tips that could different equipment in addition to historic reviews. The publication is split into elements: dense eigenproblems and massive eigenproblems. the 1st half provides a whole therapy of the commonly used QR set of rules, that is then utilized to the answer of generalized eigenproblems and the computation of the singular price decomposition. the second one half treats Krylov series tools corresponding to the Lanczos and Arnoldi algorithms and provides a brand new therapy of the Jacobi-Davidson approach. those volumes usually are not meant to be encyclopedic, yet give you the reader with the theoretical and useful heritage to learn the study literature and enforce or alter new algorithms

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Extra resources for Matrix algorithms, vol.2.. Eigensystems

Example text

Rather, we shall illustrate the basic ideas of two popular approaches: the Lanczos method (Parlett 1998) and the orthogonal reduction method (Boley and Golub 1987). We first recall the following theorem, which is the basis of the Lanczos approach. 7. The orthogonal matrix Q and the upper Hessenberg matrix H with positive subdiagonal entries can be completely determined by a given matrix A and the last (or any) column of Q if the relationship QTAQ = H holds. , An). Thus, if the last column q n is known, then the Jacobi matrix J can constructed in finitely many steps: 6n_i := ||Aq n -a n q n ||, q n _i := (Aqn - a n q n )/6 n _i, for i = 1,...

For simplicity, we set this part to be identically zero. 5) STRUCTURED INVERSE EIGENVALUE PROBLEMS 55 where K\"' is understood to be zero if i > n + 1 , for the remaining quantities. 6) where s := [ s i , . . , s n ] T , and if Ug and v^ denote column vectors of U^ and V("\ respectively. Under mild assumptions, the matrix fi(") is nonsingular. The vector c("+1) and, hence, the matrix W^ are thus obtained.

1. Some existence results The solvability of the NIEP has been the major issue of discussion in the literature. Existence results, either necessary or sufficient, are too numerous to be listed here. We shall mention only two results that, in some sense, provide the most distinct criteria in this regard. Given a matrix A, the moments of A are defined to be the sequence of numbers Sk = trace(Ak). , An}, then i=\ For nonnegative matrices, the moments are always nonnegative. The following necessary condition is due to Loewy and London (1978).

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Matrix algorithms, vol.2.. Eigensystems by G. W. Stewart

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