128 research outputs found
Approximated structured pseudospectra
Pseudospectra and structured pseudospectra are important tools for the analysis of matrices. Their computation, however, can be very demanding for all but small-matrices. A new approach to compute approximations of pseudospectra and structured pseudospectra, based on determining the spectra of many suitably chosen rank-one or projected rank-one perturbations of the given matrix is proposed. The choice of rank-one or projected rank-one perturbations is inspired by Wilkinson's analysis of eigenvalue sensitivity. Numerical examples illustrate that the proposed approach gives much better insight into the pseudospectra and structured pseudospectra than random or structured random rank-one perturbations with lower computational burden. The latter approach is presently commonly used for the determination of structured pseudospectra
A Support Based Algorithm for Optimization with Eigenvalue Constraints
Optimization of convex functions subject to eigenvalue constraints is
intriguing because of peculiar analytical properties of eigenvalues, and is of
practical interest because of wide range of applications in fields such as
structural design and control theory. Here we focus on the optimization of a
linear objective subject to a constraint on the smallest eigenvalue of an
analytical and Hermitian matrix-valued function. We offer a quadratic support
function based numerical solution. The quadratic support functions are derived
utilizing the variational properties of an eigenvalue over a set of Hermitian
matrices. Then we establish the local convergence of the algorithm under mild
assumptions, and deduce a precise rate of convergence result by viewing the
algorithm as a fixed point iteration. We illustrate its applicability in
practice on the pseudospectral functions.Comment: 18 pages, 2 figure
Computing the structured pseudospectrum of a Toeplitz matrix and its extreme points
The computation of the structured pseudospectral abscissa and radius (with
respect to the Frobenius norm) of a Toeplitz matrix is discussed and two
algorithms based on a low rank property to construct extremal perturbations are
presented. The algorithms are inspired by those considered in [SIAM J. Matrix
Anal. Appl., 32 (2011), pp. 1166-1192] for the unstructured case, but their
extension to structured pseudospectra and analysis presents several
difficulties. Natural generalizations of the algorithms, allowing to draw
significant sections of the structured pseudospectra in proximity of extremal
points are also discussed. Since no algorithms are available in the literature
to draw such structured pseudospectra, the approach we present seems promising
to extend existing software tools (Eigtool, Seigtool) to structured
pseudospectra representation for Toeplitz matrices. We discuss local
convergence properties of the algorithms and show some applications to a few
illustrative examples.Comment: 21 pages, 11 figure
Pseudospectra of waveform relaxation operators
Abstract--The performance of the waveform relaxation method for solving systems of ODEs depends largely on the choices that are made for splitting, size of time window, and preconditioning. Although it is known that superlinear convergence is obtained on finite time windows, the convergence may be slow in the first few iterations. We propose the use of pseudcepectra to analyze the convergence ratio of the first few iterations when waveform relaxation is applied to linear systems of ODEs. Through pseudcepectral radii, we can examine the effect of preconditioning and overlapping on the rate of convergence. We may also use this to estimate a suitable size of the time window. Numerical experiments performed on a system of ODEs arising from the discretization of an advection-diffusion equation confirm the validity of the obtained estimates. (~) 1998 Elsevier Science Ltd. All rights reserved
Approximating the Real Structured Stability Radius with Frobenius Norm Bounded Perturbations
We propose a fast method to approximate the real stability radius of a linear
dynamical system with output feedback, where the perturbations are restricted
to be real valued and bounded with respect to the Frobenius norm. Our work
builds on a number of scalable algorithms that have been proposed in recent
years, ranging from methods that approximate the complex or real pseudospectral
abscissa and radius of large sparse matrices (and generalizations of these
methods for pseudospectra to spectral value sets) to algorithms for
approximating the complex stability radius (the reciprocal of the
norm). Although our algorithm is guaranteed to find only upper bounds to the
real stability radius, it seems quite effective in practice. As far as we know,
this is the first algorithm that addresses the Frobenius-norm version of this
problem. Because the cost mainly consists of computing the eigenvalue with
maximal real part for continuous-time systems (or modulus for discrete-time
systems) of a sequence of matrices, our algorithm remains very efficient for
large-scale systems provided that the system matrices are sparse
The effect of uncertainty in MEG-to-MRI coregistrations on MEG inverse problems
For high precision in source estimates of magnetoencephalography (MEG) data, high accuracy of the coregistration of sources and sensors is mandatory. Usually, the source space is derived from magnetic resonance imaging (MRI). Sensor-to-MRI coregistrations are the focus of this thesis. The quality of coregistrations is assessed and the effect of their uncertainties on source estimates is analyzed. Both topics, the quality assessment and the propagation of uncertainties to source estimates are treated separately. In this thesis, the target registration error (TRE) is proposed as criterion for the quality of sensor-to-MRI coregistrations. TRE measures the effect of uncertainty in coregistrations at all points of interest. In total, 5544 data sets with sensor-to-head and 128 head-to-MRI coregistrations, from a single MEG laboratory, were analyzed. An adaptive Metropolis algorithm was used to estimate the optimal coregistration and to sample the coregistration parameters (rotation and translation). I found an average TRE between 1.3 and 2.3 mm at the head surface. A mean absolute difference in coregistration parameters between the Metropolis and iterative closest point algorithm of (1.9 ± 1.5)° and (1.1 ± 0.9) mm was found. A paired sample t-test indicated a significant improvement in goal function minimization by using the Metropolis algorithm. The sampled parameters allowed computation of TRE on the entire grid of the MRI volume. Hence, I recommend the Metropolis algorithm for head-to-MRI coregistrations. The propagation of coregistration uncertainty to source estimates was performed by using pseudospectral approximations of beamformer and standardized low resolution tomography (sLORETA). This approach was tested for auditory, visual and somatosensory brain activity with different signal to noise ratios and source orientation constraints on datasets of 20 subjects. By using pseudospectral approximations as efficient surrogates, the spatial distribution of the source estimate maximum was sampled for 50000 coregistrations. From the results, it can be concluded that it is possible to apply stochastic spectral methods to MEG source estimation with high accuracy. The investigated effects of coregistration uncertainties on source estimates are small, typically the maximum location varied within a range of 5 mm, which is in the range of the localization errors. Pseudospectral approximations of the source estimates reduced computation times considerably by a factor of approximately 10000 for beamformer and 50000 for sLORETA compared to the exact original computations.FĂŒr eine hohe PrĂ€zision in der SchĂ€tzung von GehirnaktivitĂ€t, ausgehend von Daten der Magnetoenzephalographie (MEG), ist eine sehr genaue Koregistrierung der Quellen und Sensoren notwendig. Ăblicherweise werden hierbei die Quellorte der GehirnaktivitĂ€t bezĂŒglich zu Koordinaten der Magnetresonanztomographie (MRI) angegeben. Die Sensor-zu-MRI Koregistrierungen sind der Schwerpunkt dieser Arbeit. Die QualitĂ€t von Koregistrierungen wird bewertet und der Effekt ihrer Unsicherheiten auf SchĂ€tzungen der GehirnaktivitĂ€t beziehungsweise auf QuellschĂ€tzungen wird untersucht. Beide Themen, die QualitĂ€tsbewertung und die Ăbertragung der Unsicherheiten auf QuellschĂ€tzungen werden separat behandelt. In dieser Arbeit wird vorgeschlagen, den target registration error (TRE) als QualitĂ€tskriterium fĂŒr Sensor-zu-MRI Koregistrierungen zu verwenden. Der TRE kann den Effekt von Koregistrierungsunsicherheiten an beliebigen Punkten messen. Insgesamt wurden 5544 DatensĂ€tze mit Sensor-zu-Kopf und 128 DatensĂ€tze mit Kopf-zu-MRI Koregistrierungen aus einem Labor analysiert. Ein adaptiver Metropolis-Algorithmus wurde genutzt um optimale Koregistrierungen zu schĂ€tzen und um Stichproben ihrer Parameter (Rotation und Translation) zu ziehen. Es wurde ein TRE von 1.3 und 2.3 mm an der KopfoberflĂ€che gefunden. Weiter wurde eine mittlere absolute Differenz der Koregistrierungsparameter zwischen Metropolis-Algorithmus und dem etablierten iterative closest point-Algorithmus von (1.9 ± 1.5)° und (1.1 ± 0.9) mm gefunden. Ein Zweistichproben-t-Test zeigte eine signifikante Verbesserung in der Optimierung der Zielfunktion durch den Metropolis-Algorithmus. Die Ăbertragung der Koregistrierungsunsicherheit auf QuellschĂ€tzungen erfolgte unter Verwendung von speziellen Polynom-Entwicklungen des Beamformers und der standardized low resolution tomography (sLORETA). Dieser Ansatz wurde fĂŒr auditorische, visuelle und somatosensorische HirnaktivitĂ€t mit verschiedenen Signal-Rausch-VerhĂ€ltnissen und BeschrĂ€nkungen der Quellorientierung auf DatensĂ€tzen von 20 Probanden getestet. Durch die Verwendung von Polynom-Entwicklungen als effiziente Surrogate wurde die örtliche Verteilung des QuellschĂ€tzungs-Maximums fĂŒr 50000 Koregistrierungen ermittelt. Aus den Ergebnissen lĂ€sst sich schlieĂen, dass es möglich ist, Polynom-Entwicklungen mit hoher Genauigkeit auf MEG-QuellschĂ€tzungen anzuwenden. Polynom-Entwicklungen der QuellschĂ€tzungen reduzierten die Berechnungszeiten erheblich um den Faktor von etwa 10000 fĂŒr Beamformer und 50000 fĂŒr sLORETA im Vergleich zu den exakten Originalrechnungen
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