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The development of strong linearizations preserving whatever structure a matrix polynomial might possess has been a very active area of research in the last years, since such linearizations are the starting point of numerical algorithms for computing eigenvalues of structured matrix polynomials with the properties imposed by the considered structure. In this context, Hermitian matrix polynomials are one of the most important classes of matrix polynomials arising in applications and their real eigenvalues are of great interest. The sign characteristic is a set of signs attached to these real eigenvalues which is crucial for determining the behavior of systems described by Hermitian matrix polynomials and, therefore, it is desirable to develop linearizations that preserve the sign characteristic of these polynomials, but, at present, only one such linearization is known. In this paper, we present a complete characterization of all the Hermitian strong linearizations that preserve the sign characteristic of a given Hermitian matrix polynomial and identify several families of such linearizations that can be constructed very easily from the coefficients of the polynomial. [ABSTRACT FROM AUTHOR]

In this paper, we show how to perform the Hessenberg reduction of a rank structured matrix under unitary similarity operations in an efficient way, using the Givens-weight representation. This reduction can be used as a first step for eigenvalue computation. We also show how the algorithm can be modified to compute the bidiagonal reduction of a rank structured matrix, this latter method being a preprocessing step for computing the singular values of the matrix. For the main cases of interest, the algorithms we describe in this paper are of complexity O((ar + bs)n2), where n is the matrix size, r is some measure for the average rank index of the rank structure, s is some measure for the bandwidth of the unstructured matrix part around the main diagonal, and a, b ? R are certain weighting parameters. Numerical experiments demonstrate the stability of this approach. [ABSTRACT FROM AUTHOR]

In (SIAM J. Matrix Anal. Appl., 23 (2002), pp. 706–727, we developed numerical algorithms for computing sparse low-rank approximations of matrices, and we also provided a detailed error analysis of the proposed algorithms together with some numerical experiments. The low-rank approximations are constructed in a certain factored form with the degree of sparsity of the factors controlled by some user-specified parameters. In this paper, we cast the sparse low-rank approximation problem in the framework of penalized optimization problems. We discuss various approximation schemes for the penalized optimization problem which are more amenable to numerical computations. We also include some analysis to show the relations between the original optimization problem and the reduced one. We then develop a globally convergent discrete Newton-like iterative method for solving the approximate penalized optimization problems. We also compare the reconstruction errors of the sparse low-rank approximations computed by our new methods with those obtained using the methods in the earlier paper and several other existing methods for computing sparse low-rank approximations. Numerical examples show that the penalized methods are more robust and produce approximations with factors which have fewer columns and are sparser. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for accurate floating-point computation of the singular value decomposition (SVD) of the product A = BτSC, where B ∈ Rp×m, C ∈ Rq×n, S ∈ Rp×q, and p ≤ m, q ≤ n. The new algorithm uses diagonal scalings, the LU factorization with complete pivoting, the QR factorization with column pivoting, and matrix multiplication to replace A by A′ = B′τS′C′, where A and A′ have the same singular values and the matrix A′ is computed explicitly. The singular values of A′ are computed using the Jacobi SVD algorithm. It is shown that the accuracy of the new algorithm is determined by (i) the accuracy of the QR factorizations of Bτ and Cτ; (ii) the accuracy of the LU factorization with complete pivoting of S; and (iii) the accuracy of the computation of the SVD of a matrix A′ with moderate minD=diagκ2(A′D). Theoretical analysis and numerical evidence show that, in the case of rank(B) = rank(C) = p and full rank S, the accuracy of the new algorithm is unaffected by replacing B, S, C with, respectively, D1B, D2SD3, D4C, where Di, i = 1,…,4, are arbitrary diagonal matrices. As an application, the paper proposes new accurate algorithms for computing the (H,K)­SVD and (H-1,K)­SVD of S. [ABSTRACT FROM AUTHOR]

In this paper we study the problem of finding a weighted geometric mean sufficiently close to the weighted Karcher mean of positive definite matrices. This problem arises in various numerical algorithms for computing the weighted Karcher mean and in a physical problem of averaging on the Riemannian manifold of positive definite matrices. We prove that this is possible for any "convex" geometric means satisfying the Jensen-type inequality for geodesically convex functions varying over an open set of weights in the simplex of positive probability vectors. This follows from a useful estimation of the Riemannian distance between the Karcher mean and any convex geometric means. A better upper bound between the Karcher mean and the Riemannian convex combination admitting an explicit formula is presented. [ABSTRACT FROM AUTHOR]

In this paper a new O(N log³ N) solver for N x N Toeplitz-like systems, based on a divide and conquer technique, is presented. Similarly to the superfast algorithm MBA for the inversion of a Toeplitz-like matrix [R. R. Bitmead and B. D. O. Anderson, Linear Algebra Appl., 34 (1980), pp. 103-116; M. Morf, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1980, pp. 954-959], it exploits the displacement properties. In order to avoid the well-known numerical instability of the explicit inversion, the new algorithm relies on the triangular factorization and back-substitution formula for the system seen as a 2x2 block system with blocks of half size. This idea is the one used in [M. Stewart, SIAM J. Matrix Anal. Appl., 25 (2003), pp. 669-693] to improve the numerical stability of superfast methods based on the generalized Schur algorithm for positive definite Toeplitz matrices, but the algorithm we propose can be applied also to nonsymmetric Toeplitz-like systems. The stability of the algorithm is examined through numerical experiments. [ABSTRACT FROM AUTHOR]

Recently a generalization of Francis's implicitly shifted QR algorithm was proposed, notably widening the class of matrices admitting low-cost implicit QR steps. This unifying framework covered the methods and theory for Hessenberg and inverse Hessenberg matrices and furnished also new, single-shifted, QR-type methods for, e.g., CMV matrices. Convergence of this approach was only suggested by numerical experiments. No theoretical claims supporting the results were presented. In this paper we present multishift variants of these new algorithms. We also provide a convergence theory that shows that the new algorithm performs nested subspace iterations on rational Krylov subspaces. Numerical experiments confirm the validity of the theory. [ABSTRACT FROM AUTHOR]

The dqds algorithm computes all the singular values of an n x n bidiagonal matrix to high relative accuracy in O(n²) cost. Its efficient implementation is now available as a LAPACK subroutine and is the preferred algorithm for this purpose. In this paper we incorporate into dqds a technique called aggressive early deflation, which has been applied successfully to the Hessenberg QR algorithm. Extensive numerical experiments show that aggressive early deflation often reduces the dqds runtime significantly. In addition, our theoretical analysis suggests that with aggressive early deflation, the performance of dqds is largely independent of the shift strategy. We confirm through experiments that the zero-shift version is often as fast as the shifted version. We give a detailed error analysis to prove that with our proposed deflation strategy, dqds computes all the singular values to high relative accuracy. [ABSTRACT FROM AUTHOR]

In this paper, based on Patel's algorithm (1993), we propose a structure-preserving algorithm for solving palindromic quadratic eigenvalue problems (QEPs). We also show the relationship between the structure-preserving algorithm and the URV-based structure-preserving algorithm by Schröder (2007). For large sparse palindromic QEPs, we develop a generalized T-skew-Hamiltonian implicitly restarted shift-and-invert Arnoldi algorithm for solving the resulting T-skew-Hamiltonian pencils. Numerical experiments show that our proposed structure-preserving algorithms perform well on the palindromic QEP arising from a finite element model of high-speed trains and rails. [ABSTRACT FROM AUTHOR]

This paper proves the stability of a variant of Gragg's unitary Hessenberg QR algorithm (UHQR). It is shown that a single UHQR iteration with numerically unimodular shift applied to the Schur parameters and complementary parameters of a unitary Hessenberg matrix H gives computed parameters for QHHQ that are close to those obtained from a perturbed set of parameters using a perturbed shift with no numerical error. The perturbations of the parameters and the shift are bounded. [ABSTRACT FROM AUTHOR]

In this paper, we present new algorithms that can replace the diagonal entries of a Hermitian matrix by any set of diagonal entries that majorize the original set without altering the eigenvalues of the matrix. They perform this feat by applying a sequence of (N-1) or fewer plane rotations, where N is the dimension of the matrix. Both the Bendel--Mickey and the Chan--Li algorithms are special cases of the proposed procedures. Using the fact that a positive semidefinite matrix can always be factored as $\mtx{X^\adj X}$, we also provide more efficient versions of the algorithms that can directly construct factors with specified singular values and column norms. We conclude with some open problems related to the construction of Hermitian matrices with joint diagonal and spectral properties. [ABSTRACT FROM AUTHOR]

In this paper we show how to compute recursively an approximation of the left and right dominant singular subspaces of a given matrix. In order to perform as few as possible operations on each column of the matrix, we use a variant of the classical Gram­Schmidt algorithm to estimate this subspace. The method is shown to be particularly suited for matrices with many more rows than columns. Bounds for the accuracy of the computed subspace are provided. Moreover, the analysis of error propagation in this algorithm provides new insights in the loss of orthogonality typically observed in the classical Gram­Schmidt method. [ABSTRACT FROM AUTHOR]

In the convergence theory of multisplittings for symmetric positive definite (s.p.d.) matrices it is usually assumed that the weighting matrices are scalar matrices, i.e., multiples of the identity. In this paper, this restrictive condition is eliminated. In its place it is assumed that more than one (inner) iteration is performed in each processor (or block). The theory developed here is applied to nonstationary multisplittings for s.p.d. matrices, as well as to two-stage multisplittings for symmetric positive semidefinite matrices. [ABSTRACT FROM AUTHOR]

Various forms of sparse approximate inverses (SAI) have been shown to be useful for preconditioning. Their potential usefulness in a parallel environment has motivated much interest in recent years. However, the capability of an approximate inverse in eliminating the local error has not yet been fully exploited in multigrid algorithms. A careful examination of the iteration matrices of these approximate inverses indicates their superiority in smoothing the high-frequency error in addition to their inherent parallelism. We propose a new class of SAI smoothers in this paper and present their analytic smoothing factors for constant coefficient PDEs. The following are several distinctive features that make this technique special: By adjusting the quality of the approximate inverse, the smoothing factor can be improved accordingly. For hard problems, this is useful. In contrast to the ordering sensitivity of other smoothing techniques, this technique is ordering independent. In general, the sequential performance of many superior parallel algorithms is not very competitive. This technique is useful in both parallel and sequential computations. Our theoretical and numerical results have demonstrated the effectiveness of this new technique. [ABSTRACT FROM AUTHOR]

The BR algorithm, a new method for calculating the eigenvalues of an upper Hes- senberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrow-band, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the QR algorithm by a factor of 30–60 in computing time and a factor of over 100 in matrix storage space. [ABSTRACT FROM AUTHOR]