On the Linear Stability of Plane Couette Flow for an Oldroyd-B Fluid and Its
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Criss-Cross Type Algorithms for Computing the Real Pseudospectral Abscissa
CRISS-CROSS TYPE ALGORITHMS FOR COMPUTING THE REAL PSEUDOSPECTRAL ABSCISSA DING LU∗ AND BART VANDEREYCKEN∗ Abstract. The real "-pseudospectrum of a real matrix A consists of the eigenvalues of all real matrices that are "-close to A. The closeness is commonly measured in spectral or Frobenius norm. The real "-pseudospectral abscissa, which is the largest real part of these eigenvalues for a prescribed value ", measures the structured robust stability of A. In this paper, we introduce a criss-cross type algorithm to compute the real "-pseudospectral abscissa for the spectral norm. Our algorithm is based on a superset characterization of the real pseudospectrum where each criss and cross search involves solving linear eigenvalue problems and singular value optimization problems. The new algorithm is proved to be globally convergent, and observed to be locally linearly convergent. Moreover, we propose a subspace projection framework in which we combine the criss-cross algorithm with subspace projection techniques to solve large-scale problems. The subspace acceleration is proved to be locally superlinearly convergent. The robustness and efficiency of the proposed algorithms are demonstrated on numerical examples. Key words. eigenvalue problem, real pseudospectra, spectral abscissa, subspace methods, criss- cross methods, robust stability AMS subject classifications. 15A18, 93B35, 30E10, 65F15 1. Introduction. The real "-pseudospectrum of a matrix A 2 Rn×n is defined as R n×n (1) Λ" (A) = fλ 2 C : λ 2 Λ(A + E) with E 2 R ; kEk ≤ "g; where Λ(A) denotes the eigenvalues of a matrix A and k · k is the spectral norm. It is also known as the real unstructured spectral value set (see, e.g., [9, 13, 11]) and it can be regarded as a generalization of the more standard complex pseudospectrum (see, e.g., [23, 24]). -
Proquest Dissertations
RICE UNIVERSITY Magnetic Damping of an Elastic Conductor by Jeffrey M. Hokanson A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE Master of Arts APPROVED, THESIS COMMITTEE: IJU&_ Mark Embread Co-chair Associate Professor of Computational and Applied M/jmematics Steven J. Cox, (Co-chair Professor of Computational and Applied Mathematics David Damanik Associate Professor of Mathematics Houston, Texas April, 2009 UMI Number: 1466785 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. UMI® UMI Microform 1466785 Copyright 2009 by ProQuest LLC All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 ii ABSTRACT Magnetic Damping of an Elastic Conductor by Jeffrey M. Hokanson Many applications call for a design that maximizes the rate of energy decay. Typical problems of this class include one dimensional damped wave operators, where energy dissipation is caused by a damping operator acting on the velocity. Two damping operators are well understood: a multiplication operator (known as viscous damping) and a scaled Laplacian (known as Kelvin-Voigt damping). Paralleling the analysis of viscous damping, this thesis investigates energy decay for a novel third operator known as magnetic damping, where the damping is expressed via a rank-one self-adjoint operator, dependent on a function a. -
A Highly Efficient Spectral-Galerkin Method Based on Tensor Product for Fourth-Order Steklov Equation with Boundary Eigenvalue
An et al. Journal of Inequalities and Applications (2016)2016:211 DOI 10.1186/s13660-016-1158-1 R E S E A R C H Open Access A highly efficient spectral-Galerkin method based on tensor product for fourth-order Steklov equation with boundary eigenvalue Jing An1,HaiBi1 and Zhendong Luo2* *Correspondence: [email protected] Abstract 2School of Mathematics and Physics, North China Electric Power In this study, a highly efficient spectral-Galerkin method is posed for the fourth-order University, No. 2, Bei Nong Road, Steklov equation with boundary eigenvalue. By making use of the spectral theory of Changping District, Beijing, 102206, compact operators and the error formulas of projective operators, we first obtain the China Full list of author information is error estimates of approximative eigenvalues and eigenfunctions. Then we build a 1 ∩ 2 available at the end of the article suitable set of basis functions included in H0() H () and establish the matrix model for the discrete spectral-Galerkin scheme by adopting the tensor product. Finally, we use some numerical experiments to verify the correctness of the theoretical results. MSC: 65N35; 65N30 Keywords: fourth-order Steklov equation with boundary eigenvalue; spectral-Galerkin method; error estimates; tensor product 1 Introduction Increasing attention has recently been paid to numerical approximations for Steklov equa- tions with boundary eigenvalue, arising in fluid mechanics, electromagnetism, etc. (see, e.g.,[–]). However, most existing work usually treated the second-order Steklov equa- tions with boundary eigenvalue and there are relatively few articles treating fourth-order ones. The fourth-order Steklov equations with boundary eigenvalue also have been used in both mathematics and physics, for example, the main eigenvalues play a very key role in the positivity-preserving properties for the biharmonic-operator under the border conditions w = w – χwν =on∂ (see []). -
Chebyshev and Fourier Spectral Methods 2000
Chebyshev and Fourier Spectral Methods Second Edition John P. Boyd University of Michigan Ann Arbor, Michigan 48109-2143 email: [email protected] http://www-personal.engin.umich.edu/jpboyd/ 2000 DOVER Publications, Inc. 31 East 2nd Street Mineola, New York 11501 1 Dedication To Marilyn, Ian, and Emma “A computation is a temptation that should be resisted as long as possible.” — J. P. Boyd, paraphrasing T. S. Eliot i Contents PREFACE x Acknowledgments xiv Errata and Extended-Bibliography xvi 1 Introduction 1 1.1 Series expansions .................................. 1 1.2 First Example .................................... 2 1.3 Comparison with finite element methods .................... 4 1.4 Comparisons with Finite Differences ....................... 6 1.5 Parallel Computers ................................. 9 1.6 Choice of basis functions .............................. 9 1.7 Boundary conditions ................................ 10 1.8 Non-Interpolating and Pseudospectral ...................... 12 1.9 Nonlinearity ..................................... 13 1.10 Time-dependent problems ............................. 15 1.11 FAQ: Frequently Asked Questions ........................ 16 1.12 The Chrysalis .................................... 17 2 Chebyshev & Fourier Series 19 2.1 Introduction ..................................... 19 2.2 Fourier series .................................... 20 2.3 Orders of Convergence ............................... 25 2.4 Convergence Order ................................. 27 2.5 Assumption of Equal Errors ........................... -
Isospectral Families of High-Order Systems∗
Isospectral Families of High-order Systems∗ Peter Lancaster† Department of Mathematics and Statistics University of Calgary Calgary T2N 1N4, Alberta, Canada and Uwe Prells School of Mechanical, Materials, Manufacturing Engineering & Management University of Nottingham Nottingham NG7 2RD United Kingdom November 28, 2006 Keywords: Matrix Polynomials, Isospectral Systems, Structure Preserving Transformations, Palindromic Symmetries. Abstract Earlier work of the authors concerning the generation of isospectral families of second or- der (vibrating) systems is generalized to higher-order systems (with no spectrum at infinity). Results and techniques are developed first for systems without symmetries, then with Hermi- tian symmetry and, finally, with palindromic symmetry. The construction of linearizations which retain such symmetries is discussed. In both cases, the notion of strictly isospectral families of systems is introduced - implying that properties of both the spectrum and the sign-characteristic are preserved. Open questions remain in the case of strictly isospectral families of palindromic systems. Intimate connections between Hermitian and unitary sys- tems are discussed in an Appendix. ∗The authors are grateful to an anonymous reviewer for perceptive comments which led to significant improve- ments in exposition. †The research of Peter Lancaster was partly supported by a grant from the Natural Sciences and Engineering Research Council of Canada 1 1 Introduction ` By a high-order system we mean an n × n matrix polynomial L(λ) = A`λ + ··· + A1λ + A0 n×n with coefficients in C . Such a polynomial with A` 6= 0 is said to have degree `, (sometimes referred to as “order” `) and “high-order” implies that ` ≥ 2. It will be assumed throughout that the leading coefficient A` is nonsingular. -
Introduction to Spectral Methods
Introduction to spectral methods Eric Gourgoulhon Laboratoire de l’Univers et de ses Th´eories (LUTH) CNRS / Observatoire de Paris Meudon, France Based on a collaboration with Silvano Bonazzola, Philippe Grandcl´ement,Jean-Alain Marck & J´eromeNovak [email protected] http://www.luth.obspm.fr 4th EU Network Meeting, Palma de Mallorca, Sept. 2002 1 Plan 1. Basic principles 2. Legendre and Chebyshev expansions 3. An illustrative example 4. Spectral methods in numerical relativity 2 1 Basic principles 3 Solving a partial differential equation Consider the PDE with boundary condition Lu(x) = s(x); x 2 U ½ IRd (1) Bu(y) = 0; y 2 @U; (2) where L and B are linear differential operators. Question: What is a numerical solution of (1)-(2)? Answer: It is a function u¯ which satisfies (2) and makes the residual R := Lu¯ ¡ s small. 4 What do you mean by “small” ? Answer in the framework of Method of Weighted Residuals (MWR): Search for solutions u¯ in a finite-dimensional sub-space PN of some Hilbert space W (typically a L2 space). Expansion functions = trial functions : basis of PN : (Á0;:::;ÁN ) XN u¯ is expanded in terms of the trial functions: u¯(x) = u˜n Án(x) n=0 Test functions : family of functions (Â0;:::;ÂN ) to define the smallness of the residual R, by means of the Hilbert space scalar product: 8 n 2 f0;:::;Ng; (Ân;R) = 0 5 Various numerical methods Classification according to the trial functions Án: Finite difference: trial functions = overlapping local polynomials of low order Finite element: trial functions = local smooth functions (polynomial -
Math 660-Lecture 15: Spectral Methods: Fourier Pseduo-Spectral Method
Math 660-Lecture 15: Spectral methods: Fourier pseduo-spectral method The topics here will be in the book `Spectral methods in Matlab' (Chap- ter 3); Another reference book is `Spectral methods in chemistry and physics.' 1 Spectral methods v.s. pseudo-spectral methods 1.1 Spectral method A spectral method is to represent the solution of a differential equation in terms of a basis of some vector space and then reduce the differential equations to an ODE system for the coefficients. In this sense, the (contin- uous) Fourier Transform and the Finite Element methods are all spectral methods. In particular, suppose we have some equation T (u) = f(x) where T is some temporal-spatial operator for the differential equation. Then, under the basis we have the error: X X R(x) = T ( ci(t)φi(x)) − f~iφi i i Pick test functions fχjg, which is the dual basis of fφi(x)g (if the space is a Hilbert space like in FEM, these two are identical) and we require hχj;Ri = 0: This then gives a system of equations for cj. • Advantages: Reduce differential operators to multiplications, and solv- ing ODEs might be easier. • Disadvatange: The multiplication in the spatial variable becomes con- volution. Then, we may have a big matrix-vector multiplication. 1.2 Pseudo-spectral method Here, represent the solution in terms of the basis but impose the equa- tion only at discrete points. As we can see, the pseudo-spectral method in some sense is a spectral method in discrete space. There are some typical pseudo-spectral methods 1 • Fourier Pseudo-spectral method with trigonometric basis • Chebyshev pseudo-spectral method with Chebyshev polynomial basis • Collocation methods with polynomial basis (especially for ODEs solvers); (Some people also call the pseudo-spectral method the collocation method.) For discrete points, we often choose χj = δ(x − xj), so that we require R(xj) = 0: The pseudo-spectral method may have efficient algorithms, which makes solving PDEs fast. -
Validated and Numerically Efficient Chebyshev Spectral Methods For
Validated and numerically efficient Chebyshev spectral methods for linear ordinary differential equations Florent Bréhard, Nicolas Brisebarre, Mioara Joldes To cite this version: Florent Bréhard, Nicolas Brisebarre, Mioara Joldes. Validated and numerically efficient Chebyshev spectral methods for linear ordinary differential equations. ACM Transactions on Mathematical Soft- ware, Association for Computing Machinery, 2018, 44 (4), pp.44:1-44:42. 10.1145/3208103. hal- 01526272v3 HAL Id: hal-01526272 https://hal.archives-ouvertes.fr/hal-01526272v3 Submitted on 5 Jun 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Validated and numerically efficient Chebyshev spectral methods for linear ordinary differential equations Florent Bréhard∗ Nicolas Brisebarrey Mioara Joldeşz Abstract In this work we develop a validated numerics method for the solu- tion of linear ordinary differential equations (LODEs). A wide range of algorithms (i.e., Runge-Kutta, collocation, spectral methods) exist for nu- merically computing approximations of the solutions. Most of these come with proofs of asymptotic convergence, but usually, provided error bounds are non-constructive. However, in some domains like critical systems and computer-aided mathematical proofs, one needs validated effective error bounds. We focus on both the theoretical and practical complexity anal- ysis of a so-called a posteriori quasi-Newton validation method, which mainly relies on a fixed-point argument of a contracting map. -
Applying an Iterative Method Numerically to Solve N × N Matrix
Applying an iterative method numerically to solve n × n rsta.royalsocietypublishing.org matrix Wiener–Hopf equations with exponential factors Research Matthew J. Priddin1, Anastasia V. Kisil2 Article submitted to journal and Lorna J. Ayton1 1DAMTP, University of Cambridge, Cambridge, CB3 Subject Areas: 0WA, UK 2Department of Mathematics, The University wave motion, applied mathematics, of Manchester, Manchester, M13 9PL, UK differential equations Keywords: This paper presents a generalisation of a recent iterative approach to solving a class of 2 2 Wiener–Hopf equations, × matrix Wiener–Hopf equations involving exponential Riemann-Hilbert problem, iterative factors. We extend the method to square matrices of methods, scattering, n-part arbitrary dimension n, as arise in mixed boundary boundaries value problems with n junctions. To demonstrate the method we consider the classical problem of Author for correspondence: scattering a plane wave by a set of collinear plates. The results are compared to other known Matthew Priddin methods. We describe an effective implementation e-mail: [email protected] using a spectral method to compute the required Cauchy transforms. The approach is ideally suited to obtaining far-field directivity patterns of utility to applications. Convergence in iteration is fastest for large wavenumbers, but remains practical at modest wavenumbers to achieve a high degree of accuracy. 1. Introduction The Wiener–Hopf technique [1] provides a powerful tool to approach varied problems such as the solution of integral equations and random processes. Applications may so be found in such diverse fields as aeroacoustics [2], metamaterials [3], geophysics [4], crack propagation [5] and financial mathematics [6]. The mathematical problem underlying the Wiener– Hopf technique may be simply stated: find functions that are analytic in adjacent (or overlapping) regions of a complex manifold that satisfy a prescribed jump condition at their common boundary. -
Msc in Applied Mathematics
MSc in Applied Mathematics Title: Mathematical and Technological Aspects of Quantum Information Processing Author: Jordi Tura i Brugués Advisor: Sebastià Xambó i Descamps Co-Advisor: Antonio Acín dal Maschio Department: Departament de Matemàtica Aplicada II Academic year: 2010-2011 Universitat Polit`ecnicade Catalunya Facultat de Matem`atiquesi Estad´ıstica Master's Degree Thesis Mathematical and Technological Aspects of Quantum Information Processing Jordi Tura i Brugu´es Advisor: Sebasti`aXamb´oi Descamps Co-Advisor: Antonio Ac´ındal Maschio Departament de Matem`aticaAplicada II Abstract Keywords: Quantum, information, processing, computation, coding, cryptography, entanglement, field theory, entropy, key distribution, protocols. Since its genesis, quantum mechanics has proved to be a very accurate model for predicting the behavior of the world below the nanoscale. However, crucial breakthroughs in technology were needed in order to be able to effectively access and manipulate such small magnitudes. During the last twenty years, the field of quantum information processing has experienced a growing interest, in its many variants, both theoretically and practically. Despite being still at a very basic stage, expectations are high. The uniqueness of quantum phenomena (superposition of states, creation of entanglement, etc.) have no classical analogue and allow novelties such as another paradigm of computation, more secure com- munications, quantum teleportation, quantum dense coding, etc. which are presented and analyzed here. The aim of this Thesis is to present in a unified way the main mathematical methods used in quantum information processing, as well as the state of the art of their corresponding technological implementa- tions. Our contributions are based in making a self-contained presentation; seeking completeness, that is, treating the most relevant fields of research involved, focusing on their relations; and picking the most relevant, insightful references, given the quantity of literature produced in this field. -
Computation of Pseudospectral Abscissa for Large-Scale Nonlinear
IMA Journal of Numerical Analysis (2017) 37, 1831–1863 doi: 10.1093/imanum/drw065 Advance Access publication on January 11, 2017 Computation of pseudospectral abscissa for large-scale nonlinear eigenvalue problems Downloaded from https://academic.oup.com/imajna/article-abstract/37/4/1831/2894467 by Koc University user on 20 August 2019 Karl Meerbergen Department of Computer Science, KU Leuven, University of Leuven, Heverlee 3001, Belgium Emre Mengi∗ Department of Mathematics, Ko¸c University, Rumelifeneri Yolu, Sarıyer-Istanbul˙ 34450, Turkey ∗Corresponding author: [email protected] and Wim Michiels and Roel Van Beeumen Department of Computer Science, KU Leuven, University of Leuven, Heverlee 3001, Belgium [Received on 26 June 2015; revised on 21 October 2016] We present an algorithm to compute the pseudospectral abscissa for a nonlinear eigenvalue problem. The algorithm relies on global under-estimator and over-estimator functions for the eigenvalue and singular value functions involved. These global models follow from eigenvalue perturbation theory. The algorithm has three particular features. First, it converges to the globally rightmost point of the pseudospectrum, and it is immune to nonsmoothness. The global convergence assertion is under the assumption that a global lower bound is available for the second derivative of a singular value function depending on one parameter. It may not be easy to deduce such a lower bound analytically, but assigning large negative values works robustly in practice. Second, it is applicable to large-scale problems since the dominant cost per iteration stems from computing the smallest singular value and associated singular vectors, for which efficient iterative solvers can be used. -
Lecture Notes on Spectra and Pseudospectra of Matrices and Operators
Lecture Notes on Spectra and Pseudospectra of Matrices and Operators Arne Jensen Department of Mathematical Sciences Aalborg University c 2009 Abstract We give a short introduction to the pseudospectra of matrices and operators. We also review a number of results concerning matrices and bounded linear operators on a Hilbert space, and in particular results related to spectra. A few applications of the results are discussed. Contents 1 Introduction 2 2 Results from linear algebra 2 3 Some matrix results. Similarity transforms 7 4 Results from operator theory 10 5 Pseudospectra 16 6 Examples I 20 7 Perturbation Theory 27 8 Applications of pseudospectra I 34 9 Applications of pseudospectra II 41 10 Examples II 43 1 11 Some infinite dimensional examples 54 1 Introduction We give an introduction to the pseudospectra of matrices and operators, and give a few applications. Since these notes are intended for a wide audience, some elementary concepts are reviewed. We also note that one can understand the main points concerning pseudospectra already in the finite dimensional case. So the reader not familiar with operators on a separable Hilbert space can assume that the space is finite dimensional. Let us briefly outline the contents of these lecture notes. In Section 2 we recall some results from linear algebra, mainly to fix notation, and to recall some results that may not be included in standard courses on linear algebra. In Section 4 we state some results from the theory of bounded operators on a Hilbert space. We have decided to limit the exposition to the case of bounded operators.