Rational Krylov for Stieltjes matrix functions: convergence and pole selection
نویسندگان
چکیده
منابع مشابه
Rational Krylov approximation of matrix functions: Numerical methods and optimal pole selection∗
Matrix functions are a central topic of linear algebra, and problems of their numerical approximation appear increasingly often in scientific computing. We review various rational Krylov methods for the computation of large-scale matrix functions. Emphasis is put on the rational Arnoldi method and variants thereof, namely, the extended Krylov subspace method and the shift-and-invert Arnoldi met...
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To approximate f(A)b—the action of a matrix function on a vector—by a Krylov subspace method, restarts may become mandatory due to storage requirements for the Arnoldi basis or due to the growing computational complexity of evaluating f on a Hessenberg matrix of growing size. A number of restarting methods have been proposed in the literature in recent years and there has been substantial algor...
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The iterative rational Krylov algorithm (IRKA) of Gugercin et al. (2008) [8] is an interpolatory model reduction approach to the optimal H2 approximation problem. Even though the method has been illustrated to show rapid convergence in various examples, a proof of convergence has not been provided yet. In this note, we show that in the case of state-space-symmetric systems, IRKA is a locally co...
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The theorem proved here extends the author's previous work on Chebyshev series [4] by showing that if f(x) is a member of the class of so-called "Stieltjes functions" whose asymptotic power series 2 anx" about x = 0 is such that ttlogjaj _ hm —;-= r, «log n then the coefficients of the series of shifted Chebyshev polynomials on x e [0, a],2b„Tf(x/a), satisfy the inequality 2 . m log | (log |M) ...
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ژورنال
عنوان ژورنال: BIT Numerical Mathematics
سال: 2020
ISSN: 0006-3835,1572-9125
DOI: 10.1007/s10543-020-00826-z