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Orthogonal Hessenberg Reduction and Orthogonal Krylov Subspace Bases

✍ Scribed by Liesen, Jörg; Saylor, Paul E.

Book ID
118191203
Publisher
Society for Industrial and Applied Mathematics
Year
2005
Tongue
English
Weight
158 KB
Volume
42
Category
Article
ISSN
0036-1429

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The need to evaluate expressions of the form f (A)v, where A is a large sparse or structured symmetric matrix, v is a vector, and f is a nonlinear function, arises in many applications. The extended Krylov subspace method can be an attractive scheme for computing approximations of such expressions.

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This paper is devoted to further development of the method studying the condition numbers for the computation of the Krylov orthonormal bases and sub- where A is a matrix and f is a vector. The condition numbers were obtained by means of a first-order analysis of the sensitivity of the Krylov subs