On the rational approximation of Markov ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
On the rational approximation of Markov functions,with applications to the computation of Markovfunctions of Toeplitz matrices
Author(s) :
Beckermann, Bernhard [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Bisch, Joanna [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Luce, Robert [Auteur]
Gurobi Optimization
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Bisch, Joanna [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Luce, Robert [Auteur]
Gurobi Optimization
Journal title :
Numerical Algorithms
Pages :
109-144
Publisher :
Springer Verlag
Publication date :
2022
ISSN :
1017-1398
English keyword(s) :
positive Thiele continued fractions
rational interpolation
Markov function
Toeplitz matrices
matrix function
rational interpolation
Markov function
Toeplitz matrices
matrix function
HAL domain(s) :
Mathématiques [math]/Analyse numérique [math.NA]
English abstract : [en]
We investigate the problem of approximating the matrix function $f(A)$ by $r(A)$, with $f$ a Markov function, $r$ a rational interpolant of $f$, and $A$ a symmetric Toeplitz matrix. In a first step, we obtain a new upper ...
Show more >We investigate the problem of approximating the matrix function $f(A)$ by $r(A)$, with $f$ a Markov function, $r$ a rational interpolant of $f$, and $A$ a symmetric Toeplitz matrix. In a first step, we obtain a new upper bound for the relative interpolation error $1-r/f$ on the spectral interval of $A$. By minimizing this upper bound over all interpolation points, we obtain a new, simple and sharp a priori bound for the relative interpolation error. We then consider three different approaches of representing and computing the rational interpolant $r$. Theoretical and numerical evidence is given that any of these methods for a scalar argument allows to achieve high precision, even in the presence of finite precision arithmetic. We finally investigate the problem of efficiently evaluating $r(A)$, where it turns out that the relative error for a matrix argument is only small if we use a partial fraction decomposition for $r$ following Antoulas and Mayo. An important role is played by a new stopping criterion which ensures to automatically find the degree of $r$ leading to a small error, even in presence of finite precision arithmetic.Show less >
Show more >We investigate the problem of approximating the matrix function $f(A)$ by $r(A)$, with $f$ a Markov function, $r$ a rational interpolant of $f$, and $A$ a symmetric Toeplitz matrix. In a first step, we obtain a new upper bound for the relative interpolation error $1-r/f$ on the spectral interval of $A$. By minimizing this upper bound over all interpolation points, we obtain a new, simple and sharp a priori bound for the relative interpolation error. We then consider three different approaches of representing and computing the rational interpolant $r$. Theoretical and numerical evidence is given that any of these methods for a scalar argument allows to achieve high precision, even in the presence of finite precision arithmetic. We finally investigate the problem of efficiently evaluating $r(A)$, where it turns out that the relative error for a matrix argument is only small if we use a partial fraction decomposition for $r$ following Antoulas and Mayo. An important role is played by a new stopping criterion which ensures to automatically find the degree of $r$ leading to a small error, even in presence of finite precision arithmetic.Show less >
Language :
Anglais
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Non
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