Given matrices A and B such that B = f (A), where f (z) is a holomorphic function, we analyze the relation between the singular values of the off-diagonal submatrices of A and B. We provide a family of bounds which de...
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Given matrices A and B such that B = f (A), where f (z) is a holomorphic function, we analyze the relation between the singular values of the off-diagonal submatrices of A and B. We provide a family of bounds which depend on the interplay between the spectrum of the argument A and the singularities of the function. In particular, these bounds guarantee the numerical preservation of quasiseparable structures under mild hypotheses. We extend the Dunford-Cauchy integral formula to the case in which some Poles are contained inside the contour of integration. We use this tool together with the technology of hierarchical matrices (H-matrices) for the effective computation of matrix functions with quasiseparable arguments. (C) 2016 Elsevier Inc. All rights reserved.
In recent years, there has been a resurgence in the construction and implementation of exponential integrators. which are numerical methods specifically designed for he numerical solution of spatially discretized semi...
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In recent years, there has been a resurgence in the construction and implementation of exponential integrators. which are numerical methods specifically designed for he numerical solution of spatially discretized semi-linear partial differential equations. Exponential integrators use the matrix exponential and related matrix functions within the formulation of the numerical method. The scaling and squaring method is the most widely used method for computing the matrix exponential. The aim of this paper is to discuss the efficient and accurate evaluation of the matrix exponential and related matrix functions Using a scaling and modified squaring method. (c) 2008 IMACS. Published by Elsevier B.V. All rights reserved.
We discuss the efficient computation of performance, reliability, and availability measures for Markov chains;these metrics - and the ones obtained by combining them, are often called performability measures. We show ...
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We discuss the efficient computation of performance, reliability, and availability measures for Markov chains;these metrics - and the ones obtained by combining them, are often called performability measures. We show that this computational problem can be recasted as the evaluation of a bilinear form induced by appropriate matrix functions, and thus solved by leveraging the fast methods available for this task. We provide a comprehensive analysis of the theory required to translate the problem from the language of Markov chains to the one of matrix functions. The advantages of this new formulation are discussed, and it is shown that this setting allows to easily study the sensitivities of the measures with respect to the model parameters. Numerical experiments confirm the effectiveness of our approach;the tests we have run show that we can outperform the solvers available in state of the art commercial packages on a representative set of large scale examples. (C) 2019 Elsevier B.V. All rights reserved.
A technique for the recursive inversion of matrices and matrix functions is presented. The proposed method can be modelled as a discrete nonlinear dynamic system. Convergence, stability and robustness properties are d...
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A technique for the recursive inversion of matrices and matrix functions is presented. The proposed method can be modelled as a discrete nonlinear dynamic system. Convergence, stability and robustness properties are discussed and eventually verified through various numerical experiments.
In this paper we are interested in the polynomial Krylov approximations for the computation of phi(A)upsilon, where A is a square matrix, v represents a given vector, and. is a suitable function which can be employed ...
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In this paper we are interested in the polynomial Krylov approximations for the computation of phi(A)upsilon, where A is a square matrix, v represents a given vector, and. is a suitable function which can be employed in modern integrators for differential problems. Our aim consists of proposing and analyzing innovative a posteriori error estimates which allow a good control of the approximation procedure. The effectiveness of the results we provide is tested on some numerical examples of interest.
When A is a Hermitian matrix, the action f(A)b of a matrix function f(A) on a vector b can efficiently be approximated via the Lanczos method. In this note we use M-matrix theory to establish that the 2-norm of the er...
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When A is a Hermitian matrix, the action f(A)b of a matrix function f(A) on a vector b can efficiently be approximated via the Lanczos method. In this note we use M-matrix theory to establish that the 2-norm of the error of the sequence of approximations is monotonically decreasing if f is a Stieltjes transform and A is positive definite. We discuss the relation of our approach to a recent, more general monotonicity result of Druskin for Laplace transforms. We also extend the class of functions to certain product type functions. This yields, for example, monotonicity when approximating sign(A)b with A indefinite if the Lanczos method is performed for A(2) rather than A.
We analyze one-dimensional discrete and quasi-continuous linear chains of N >> 1 equidistant and identical mass points with periodic boundary conditions and generalized nonlocal interparticle interactions in the...
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We analyze one-dimensional discrete and quasi-continuous linear chains of N >> 1 equidistant and identical mass points with periodic boundary conditions and generalized nonlocal interparticle interactions in the harmonic approximation. We introduce elastic potentials which define by Hamilton's principle discrete "Laplacian operators" ("Laplacian matrices") which are operator functions (N x N-matrix functions) of the Laplacian of the Born-von-Karman linear chain with next neighbor interactions. The non-locality of the constitutive law of the present model is a natural consequence of the non-diagonality of these Laplacian matrix functions in the N dimensional vector space of particle displacement fields where the periodic boundary conditions (cyclic boundary conditions) and as a consequence the (Bloch-)eigenvectors of the linear chain are maintained. In the quasi-continuum limit (long-wave limit) the Laplacian matrices yield "Laplacian convolution kernels" (and the related elastic modulus kernels) of the non-local constitutive law. The elastic stability is guaranteed by the positiveness of the elastic potentials. We establish criteria for "weak" and "strong" nonlocality of the constitutive behavior which can be controlled by scaling behavior of material constants in the continuum limit when the interparticle spacing h -> 0. The approach provides a general method to generate physically admissible (elastically stable) non-local constitutive laws by means of "simple" Laplacian matrix functions. The model can be generalized to model non-locality in n = 2, 3, ... dimensions of the physical space. (C) 2014 Elsevier Ltd. All rights reserved.
Identifying important components in a network is one of the major goals of network analysis. Popular and effective measures of importance of a node or a set of nodes are defined in terms of suitable entries of functio...
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Identifying important components in a network is one of the major goals of network analysis. Popular and effective measures of importance of a node or a set of nodes are defined in terms of suitable entries of functions of matrices f(A). These kinds of measures are particularly relevant as they are able to capture the global structure of connections involving a node. However, computing the entries of f(A) requires a significant computational effort. In this work we address the problem of estimating the changes in the entries of f(A) with respect to changes in the edge structure. Intuition suggests that, if the topology, or the overall weight, of the connections in the new graph (G) over tilde is not significantly distorted, relevant components in G maintain their leading role in (G) over tilde. We propose several bounds giving mathematical reasoning to such intuition and showing, in particular, that the magnitude of the variation of the entry f(A)(kl) decays exponentially with the shortest-path distance in G that separates either k or l from the set of nodes touched by the edges that are perturbed. Moreover, we propose a simple method that exploits the computation of f(A) to simultaneously compute the all-pairs shortest-path distances of G, with essentially no additional cost. The proposed hounds are particularly relevant when the nodes whose edge connection tends to change more often or tends to be more often affected by noise have a marginal role in the graph and are distant from the most central nodes.
The computation of matrix functions f(A), or related quantities like their trace, is an important but challenging task, in particular, for large and sparse matrices A. In recent years, probing methods have become an o...
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The computation of matrix functions f(A), or related quantities like their trace, is an important but challenging task, in particular, for large and sparse matrices A. In recent years, probing methods have become an often considered tool in this context, as they allow one to replace the computation of f(A) or tr(f(A)) by the evaluation of (a small number of) quantities of the form f(A)v or v(T) f(A)(v), respectively. These quantities can then be efficiently computed by standard techniques like, e.g., Krylov subspace methods. It is well known that probing methods are particularly efficient when f(A) is approximately sparse, e.g., when the entries of f(A) show a strong off-diagonal decay, but a rigorous error analysis is lacking so far. In this paper we develop new theoretical results on the existence of sparse approximations for f(A) and error bounds for probing methods based on graph colorings. As a by-product, by carefully inspecting the proofs of these error bounds, we also gain new insights into when to stop the Krylov iteration used for approximating f(A)v or v(T) f(A)(v), thus allowing for a practically efficient implementation of the probing methods.
Building upon earlier work by Golub, Meurant, Strakos, and Tichy, we derive new a posteriori error bounds for Krylov subspace approximations to f(A)b, the action of a function f of a real symmetric or complex Hermitia...
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Building upon earlier work by Golub, Meurant, Strakos, and Tichy, we derive new a posteriori error bounds for Krylov subspace approximations to f(A)b, the action of a function f of a real symmetric or complex Hermitian matrix A on a vector b. To this purpose we assume that a rational function in partial fraction expansion form is used to approximate f, and the Krylov subspace approximations are obtained as linear combinations of Galerkin approximations to the individual terms in the partial fraction expansion. Our error estimates come at very low computational cost. In certain important special situations they can be shown to actually be lower bounds of the error. Our numerical results include experiments with the matrix exponential, as used in exponential integrators, and with the matrix sign function, as used in lattice quantum chromodynamics simulations, and demonstrate the accuracy of the estimates. The use of our error estimates within acceleration procedures is also discussed.
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