arXiv subjects

María C. Quintana

Publications and source records attributed to María C. Quintana.

13 recordsLinked to original sources

arXiv2026

A Fast and Stable Square-Root-Free Unitary Core-Chasing Algorithm

Symmetric and unitary matrices are among the most important classes of structured matrices admitting efficient and stable eigenvalue algorithms. Notable examples include structure-preserving variants of the QR algorithm and their square-root-free counterparts. For unitary matrices, these algorithms were historically derived from recurrence relations for orthogonal polynomials on the unit circle. A matrix-based derivation using core chasing was later given in [3]; this family of algorithms is backward stable, but the underlying symmetries needed to cast out the square roots were not identified, and the question of structured backward stability was left open. In this paper we present a matrix-based derivation of the symmetric unitary core-chasing algorithm and use it to obtain a square-root-free variant. We give a matrix-based proof of backward stability and show that strict structured backward stability is impossible in general. Open-source Fortran code and numerical experiments demonstrate that the combination of exploiting the symmetry and removing the square roots reduces computation time without degrading accuracy.

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arXiv2024

Para-Hermitian rational matrices

In this paper we study para-Hermitian rational matrices and the associated structured rational eigenvalue problem (REP). Para-Hermitian rational matrices are square rational matrices that are Hermitian for all $z$ on the unit circle that are not poles. REPs are often solved via linearization, that is, using matrix pencils associated to the corresponding rational matrix that preserve the spectral structure. Yet, non-constant polynomial matrices cannot be para-Hermitian. Therefore, given a para-Hermitian rational matrix $R(z)$, we instead construct a $*$-palindromic linearization for $(1+z)R(z)$, whose eigenvalues that are not on the unit circle preserve the symmetries of the zeros and poles of $R(z)$. This task is achieved via Möbius transformations. We also give a constructive method that is based on an additive decomposition into the stable and anti-stable parts of $R(z)$. Analogous results are presented for para-skew-Hermitian rational matrices, i.e., rational matrices that are skew-Hermitian upon evaluation on those points of the unit circle that are not poles.

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arXiv2023

Generating functions of non-backtracking walks on weighted digraphs: radius of convergence and Ihara's theorem

It is known that the generating function associated with the enumeration of non-backtracking walks on finite graphs is a rational matrix-valued function of the parameter; such function is also closely related to graph-theoretical results such as Ihara's theorem and the zeta function on graphs. In [P. Grindrod, D. J. Higham, V. Noferini, The deformed graph Laplacian and its application to network centrality analysis, SIAM J. Matrix Anal. Appl. 39(1), 310--341, 2018], the radius of convergence of the generating function was studied for simple (i.e., undirected, unweighted and with no loops) graphs, and shown to depend on the number of cycles in the graph. In this paper, we use technologies from the theory of polynomial and rational matrices to greatly extend these results by studying the radius of convergence of the corresponding generating function for general, possibly directed and/or weighted, graphs. We give an analogous characterization of the radius of convergence for directed unweighted graphs, showing that it depends on the number of cycles in the undirectization of the graph. For weighted graphs, we provide for the first time an exact formula for the radius of convergence, improving a previous result that exhibited a lower bound. Finally, we consider also backtracking-downweighted walks on unweighted digraphs, and we prove a version of Ihara's theorem in that case.

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arXiv2022

Linearizations of matrix polynomials viewed as Rosenbrock's system matrices

A well known method to solve the Polynomial Eigenvalue Problem (PEP) is via linearization. That is, transforming the PEP into a generalized linear eigenvalue problem with the same spectral information and solving such linear problem with some of the eigenvalue algorithms available in the literature. Linearizations of matrix polynomials are usually defined using unimodular transformations. In this paper we establish a connection between the standard definition of linearization for matrix polynomials introduced by Gohberg, Lancaster and Rodman and the notion of polynomial system matrix introduced by Rosenbrock. This connection gives new techniques to show that a matrix pencil is a linearization of the corresponding matrix polynomial arising in a PEP.

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arXiv2022

Perturbation theory of transfer function matrices

Zeros of rational transfer function matrices $R(λ)$ are the eigenvalues of associated polynomial system matrices $P(λ)$, under minimality conditions. In this paper we define a structured condition number for a simple eigenvalue $λ_0$ of a (locally) minimal polynomial system matrix $P(λ)$, which in turn is a simple zero $λ_0$ of its transfer function matrix $R(λ)$. Since any rational matrix can be written as the transfer function of a polynomial system matrix, our analysis yield a structured perturbation theory for simple zeros of rational matrices $R(λ)$. To capture all the zeros of $R(λ)$, regardless of whether they are poles or not, we consider the notion of root vectors. As corollaries of the main results, we pay particular attention to the special case of $λ_0$ being not a pole of $R(λ)$ since in this case the results get simpler and can be useful in practice. We also compare our structured condition number with Tisseur's unstructured condition number for eigenvalues of matrix polynomials, and show that the latter can be unboundedly larger. Finally, we corroborate our analysis by numerical experiments.

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arXiv2021

Strongly minimal self-conjugate linearizations for polynomial and rational matrices

We prove that we can always construct strongly minimal linearizations of an arbitrary rational matrix from its Laurent expansion around the point at infinity, which happens to be the case for polynomial matrices expressed in the monomial basis. If the rational matrix has a particular self-conjugate structure we show how to construct strongly minimal linearizations that preserve it. The structures that are considered are the Hermitian and skew-Hermitian rational matrices with respect to the real line, and the para-Hermitian and para-skew-Hermitian matrices with respect to the imaginary axis. We pay special attention to the construction of strongly minimal linearizations for the particular case of structured polynomial matrices. The proposed constructions lead to efficient numerical algorithms for constructing strongly minimal linearizations. The fact that they are valid for {\em any} rational matrix is an improvement on any other previous approach for constructing other classes of structure preserving linearizations, which are not valid for any structured rational or polynomial matrix. The use of the recent concept of strongly minimal linearization is the key for getting such generality.

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arXiv2021

Diagonal scalings for the eigenstructure of arbitrary pencils

In this paper we show how to construct diagonal scalings for arbitrary matrix pencils $λB-A$, in which both $A$ and $B$ are complex matrices (square or nonsquare). The goal of such diagonal scalings is to "balance" in some sense the row and column norms of the pencil. We see that the problem of scaling a matrix pencil is equivalent to the problem of scaling the row and column sums of a particular nonnegative matrix. However, it is known that there exist square and nonsquare nonnegative matrices that can not be scaled arbitrarily. To address this issue, we consider an approximate embedded problem, in which the corresponding nonnegative matrix is square and can always be scaled. The new scaling methods are then based on the Sinkhorn-Knopp algorithm for scaling a square nonnegative matrix with total support to be doubly stochastic or on a variant of it. In addition, using results of U. G. Rothblum and H. Schneider (1989), we give simple sufficient conditions on the zero pattern for the existence of diagonal scalings of square nonnegative matrices to have any prescribed common vector for the row and column sums. We illustrate numerically that the new scaling techniques for pencils improve the accuracy of the computation of their eigenvalues.

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arXiv2021

Structural backward stability in rational eigenvalue problems solved via block Kronecker linearizations

We study the backward stability of running a backward stable eigenstructure solver on a pencil $S(λ)$ that is a strong linearization of a rational matrix $R(λ)$ expressed in the form $R(λ)=D(λ)+ C(λI_\ell-A)^{-1}B$, where $D(λ)$ is a polynomial matrix and $C(λI_\ell-A)^{-1}B$ is a minimal state-space realization. We consider the family of block Kronecker linearizations of $R(λ)$, which are highly structured pencils. Backward stable eigenstructure solvers applied to $S(λ)$ will compute the exact eigenstructure of a perturbed pencil $\widehat S(λ):=S(λ)+Δ_S(λ)$ and the special structure of $S(λ)$ will be lost. In order to link this perturbed pencil with a nearby rational matrix, we construct a strictly equivalent pencil $\widetilde S(λ)$ to $\widehat S(λ)$ that restores the original structure, and hence is a block Kronecker linearization of a perturbed rational matrix $\widetilde R(λ) = \widetilde D(λ)+ \widetilde C(λI_\ell- \widetilde A)^{-1} \widetilde B$, where $\widetilde D(λ)$ is a polynomial matrix with the same degree as $D(λ)$. Moreover, we bound appropriate norms of $\widetilde D(λ)- D(λ)$, $\widetilde C - C$, $\widetilde A - A$ and $\widetilde B - B$ in terms of an appropriate norm of $Δ_S(λ)$. These bounds may be inadmissibly large, but we also introduce a scaling that allows us to make them satisfactorily tiny. Thus, for this scaled representation, we prove that the staircase and the $QZ$ algorithms compute the exact eigenstructure of a rational matrix $\widetilde R(λ)$ that can be expressed in exactly the same form as $R(λ)$ with the parameters defining the representation very near to those of $R(λ)$. This shows that this approach is backward stable in a structured sense.

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arXiv2021

Linear system matrices of rational transfer functions

In this paper we derive new sufficient conditions for a linear system matrix $$S(λ):=\left[\begin{array}{ccc} T(λ) & -U(λ) \\ V(λ) & W(λ) \end{array}\right],$$ where $T(λ)$ is assumed regular, to be strongly irreducible. In particular, we introduce the notion of strong minimality, and the corresponding conditions are shown to be sufficient for a polynomial system matrix to be strongly minimal. A strongly irreducible or minimal system matrix has the same structural elements as the rational matrix $R(λ)= W(λ) + V(λ)T(λ)^{-1}U(λ)$, which is also known as the transfer function connected to the system matrix $S(λ)$. The pole structure, zero structure and null space structure of $R(λ)$ can be then computed with the staircase algorithm and the $QZ$ algorithm applied to pencils derived from $S(λ)$. We also show how to derive a strongly minimal system matrix from an arbitrary linear system matrix by applying to it a reduction procedure, that only uses unitary equivalence transformations. This implies that numerical errors performed during the reduction procedure remain bounded. Finally, we show how to perform diagonal scalings to an arbitrary pencil such that its row and column norms are all of the order of 1. Combined with the fact that we use unitary transformation in both the reduction procedure and the computation of the eigenstructure, this guarantees that we computed the exact eigenstructure of a perturbed linear system matrix, but where the perturbation is of the order of the machine precision.

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arXiv2020

Block Full Rank Linearizations of Rational Matrices

Block full rank pencils introduced in [Dopico et al., Local linearizations of rational matrices with application to rational approximations of nonlinear eigenvalue problems, Linear Algebra Appl., 2020] allow us to obtain local information about zeros that are not poles of rational matrices. In this paper we extend the structure of those block full rank pencils to construct linearizations of rational matrices that allow us to recover locally not only information about zeros but also about poles, whenever certain minimality conditions are satisfied. In addition, the notion of degree of a rational matrix will be used to determine the grade of the new block full rank linearizations as linearizations at infinity. This new family of linearizations is important as it generalizes and includes the structures appearing in most of the linearizations for rational matrices constructed in the literature. In particular, this theory will be applied to study the structure and the properties of the linearizations in [P. Lietaert et al., Automatic rational approximation and linearization of nonlinear eigenvalue problems, submitted].

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arXiv2020

Linearizations of rational matrices from general representations

We construct a new family of linearizations of rational matrices $R(λ)$ written in the general form $R(λ)= D(λ)+C(λ)A(λ)^{-1}B(λ)$, where $D(λ)$, $C(λ)$, $B(λ)$ and $A(λ)$ are polynomial matrices. Such representation always exists and are not unique. The new linearizations are constructed from linearizations of the polynomial matrices $D(λ)$ and $A(λ)$, where each of them can be represented in terms of any polynomial basis. In addition, we show how to recover eigenvectors, when $R(λ)$ is regular, and minimal bases and minimal indices, when $R(λ)$ is singular, from those of their linearizations in this family.

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arXiv2019

Local Linearizations of Rational Matrices with Application to Rational Approximations of Nonlinear Eigenvalue Problems

This paper presents a definition for local linearizations of rational matrices and studies their properties. This definition allows us to introduce matrix pencils associated to a rational matrix that preserve its structure of zeros and poles in subsets of any algebraically closed field and also at infinity. Moreover, such definition includes, as particular cases, other definitions that have been used previously in the literature. In this way, this new theory of local linearizations captures and explains rigorously the properties of all the different pencils that have been used from the 1970's until 2019 for computing zeros, poles and eigenvalues of rational matrices. Particular attention is paid to those pencils that have appeared recently in the numerical solution of nonlinear eigenvalue problems through rational approximation.

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arXiv2018

Strong linearizations of rational matrices with polynomial part expressed in an orthogonal basis

We construct a new family of strong linearizations of rational matrices considering the polynomial part of them expressed in a basis that satisfies a three term recurrence relation. For this purpose, we combine the theory developed by Amparan et al., MIMS EPrint 2016.51, and the new linearizations of polynomial matrices introduced by Faßbender and Saltenberger, Linear Algebra Appl., 525 (2017). In addition, we present a detailed study of how to recover eigenvectors of a rational matrix from those of its linearizations in this family. We complete the paper by discussing how to extend the results when the polynomial part is expressed in other bases, and by presenting strong linearizations that preserve the structure of symmetric or Hermitian rational matrices. A conclusion of this work is that the combination of the results in this paper with those in Amparan et al., MIMS EPrint 2016.51, allows us to use essentially all the strong linearizations of polynomial matrices developed in the last fifteen years to construct strong linearizations of any rational matrix by expressing such matrix in terms of its polynomial and strictly proper parts.

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