209. "Fast and Stable QR Eigenvalue Algorithms for Generalized
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2 Homework Solutions 18.335 " Fall 2004
2 Homework Solutions 18.335 - Fall 2004 2.1 Count the number of ‡oating point operations required to compute the QR decomposition of an m-by-n matrix using (a) Householder re‡ectors (b) Givens rotations. 2 (a) See Trefethen p. 74-75. Answer: 2mn2 n3 ‡ops. 3 (b) Following the same procedure as in part (a) we get the same ‘volume’, 1 1 namely mn2 n3: The only di¤erence we have here comes from the 2 6 number of ‡opsrequired for calculating the Givens matrix. This operation requires 6 ‡ops (instead of 4 for the Householder re‡ectors) and hence in total we need 3mn2 n3 ‡ops. 2.2 Trefethen 5.4 Let the SVD of A = UV : Denote with vi the columns of V , ui the columns of U and i the singular values of A: We want to …nd x = (x1; x2) and such that: 0 A x x 1 = 1 A 0 x2 x2 This gives Ax2 = x1 and Ax1 = x2: Multiplying the 1st equation with A 2 and substitution of the 2nd equation gives AAx2 = x2: From this we may conclude that x2 is a left singular vector of A: The same can be done to see that x1 is a right singular vector of A: From this the 2m eigenvectors are found to be: 1 v x = i ; i = 1:::m p2 ui corresponding to the eigenvalues = i. Therefore we get the eigenvalue decomposition: 1 0 A 1 VV 0 1 VV = A 0 p2 U U 0 p2 U U 3 2.3 If A = R + uv, where R is upper triangular matrix and u and v are (column) vectors, describe an algorithm to compute the QR decomposition of A in (n2) time. -
A Fast Algorithm for the Recursive Calculation of Dominant Singular Subspaces N
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Computational and Applied Mathematics 218 (2008) 238–246 www.elsevier.com/locate/cam A fast algorithm for the recursive calculation of dominant singular subspaces N. Mastronardia,1, M. Van Barelb,∗,2, R. Vandebrilb,2 aIstituto per le Applicazioni del Calcolo, CNR, via Amendola122/D, 70126, Bari, Italy bDepartment of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium Received 26 September 2006 Abstract In many engineering applications it is required to compute the dominant subspace of a matrix A of dimension m × n, with m?n. Often the matrix A is produced incrementally, so all the columns are not available simultaneously. This problem arises, e.g., in image processing, where each column of the matrix A represents an image of a given sequence leading to a singular value decomposition-based compression [S. Chandrasekaran, B.S. Manjunath, Y.F. Wang, J. Winkeler, H. Zhang, An eigenspace update algorithm for image analysis, Graphical Models and Image Process. 59 (5) (1997) 321–332]. Furthermore, the so-called proper orthogonal decomposition approximation uses the left dominant subspace of a matrix A where a column consists of a time instance of the solution of an evolution equation, e.g., the flow field from a fluid dynamics simulation. Since these flow fields tend to be very large, only a small number can be stored efficiently during the simulation, and therefore an incremental approach is useful [P. Van Dooren, Gramian based model reduction of large-scale dynamical systems, in: Numerical Analysis 1999, Chapman & Hall, CRC Press, London, Boca Raton, FL, 2000, pp. -
A Fast Method for Computing the Inverse of Symmetric Block Arrowhead Matrices
Appl. Math. Inf. Sci. 9, No. 2L, 319-324 (2015) 319 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.12785/amis/092L06 A Fast Method for Computing the Inverse of Symmetric Block Arrowhead Matrices Waldemar Hołubowski1, Dariusz Kurzyk1,∗ and Tomasz Trawi´nski2 1 Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44–100, Poland 2 Mechatronics Division, Silesian University of Technology, Akademicka 10a, Gliwice 44–100, Poland Received: 6 Jul. 2014, Revised: 7 Oct. 2014, Accepted: 8 Oct. 2014 Published online: 1 Apr. 2015 Abstract: We propose an effective method to find the inverse of symmetric block arrowhead matrices which often appear in areas of applied science and engineering such as head-positioning systems of hard disk drives or kinematic chains of industrial robots. Block arrowhead matrices can be considered as generalisation of arrowhead matrices occurring in physical problems and engineering. The proposed method is based on LDLT decomposition and we show that the inversion of the large block arrowhead matrices can be more effective when one uses our method. Numerical results are presented in the examples. Keywords: matrix inversion, block arrowhead matrices, LDLT decomposition, mechatronic systems 1 Introduction thermal and many others. Considered subsystems are highly differentiated, hence formulation of uniform and simple mathematical model describing their static and A square matrix which has entries equal zero except for dynamic states becomes problematic. The process of its main diagonal, a one row and a column, is called the preparing a proper mathematical model is often based on arrowhead matrix. Wide area of applications causes that the formulation of the equations associated with this type of matrices is popular subject of research related Lagrangian formalism [9], which is a convenient way to with mathematics, physics or engineering, such as describe the equations of mechanical, electromechanical computing spectral decomposition [1], solving inverse and other components. -
Deflation by Restriction for the Inverse-Free Preconditioned Krylov Subspace Method
NUMERICAL ALGEBRA, doi:10.3934/naco.2016.6.55 CONTROL AND OPTIMIZATION Volume 6, Number 1, March 2016 pp. 55{71 DEFLATION BY RESTRICTION FOR THE INVERSE-FREE PRECONDITIONED KRYLOV SUBSPACE METHOD Qiao Liang Department of Mathematics University of Kentucky Lexington, KY 40506-0027, USA Qiang Ye∗ Department of Mathematics University of Kentucky Lexington, KY 40506-0027, USA (Communicated by Xiaoqing Jin) Abstract. A deflation by restriction scheme is developed for the inverse-free preconditioned Krylov subspace method for computing a few extreme eigen- values of the definite symmetric generalized eigenvalue problem Ax = λBx. The convergence theory for the inverse-free preconditioned Krylov subspace method is generalized to include this deflation scheme and numerical examples are presented to demonstrate the convergence properties of the algorithm with the deflation scheme. 1. Introduction. The definite symmetric generalized eigenvalue problem for (A; B) is to find λ 2 R and x 2 Rn with x 6= 0 such that Ax = λBx (1) where A; B are n × n symmetric matrices and B is positive definite. The eigen- value problem (1), also referred to as a pencil eigenvalue problem (A; B), arises in many scientific and engineering applications, such as structural dynamics, quantum mechanics, and machine learning. The matrices involved in these applications are usually large and sparse and only a few of the eigenvalues are desired. Iterative methods such as the Lanczos algorithm and the Arnoldi algorithm are some of the most efficient numerical methods developed in the past few decades for computing a few eigenvalues of a large scale eigenvalue problem, see [1, 11, 19]. -
Numerical Linear Algebra Revised February 15, 2010 4.1 the LU
Numerical Linear Algebra Revised February 15, 2010 4.1 The LU Decomposition The Elementary Matrices and badgauss In the previous chapters we talked a bit about the solving systems of the form Lx = b and Ux = b where L is lower triangular and U is upper triangular. In the exercises for Chapter 2 you were asked to write a program x=lusolver(L,U,b) which solves LUx = b using forsub and backsub. We now address the problem of representing a matrix A as a product of a lower triangular L and an upper triangular U: Recall our old friend badgauss. function B=badgauss(A) m=size(A,1); B=A; for i=1:m-1 for j=i+1:m a=-B(j,i)/B(i,i); B(j,:)=a*B(i,:)+B(j,:); end end The heart of badgauss is the elementary row operation of type 3: B(j,:)=a*B(i,:)+B(j,:); where a=-B(j,i)/B(i,i); Note also that the index j is greater than i since the loop is for j=i+1:m As we know from linear algebra, an elementary opreration of type 3 can be viewed as matrix multipli- cation EA where E is an elementary matrix of type 3. E looks just like the identity matrix, except that E(j; i) = a where j; i and a are as in the MATLAB code above. In particular, E is a lower triangular matrix, moreover the entries on the diagonal are 1's. We call such a matrix unit lower triangular. -
Massively Parallel Poisson and QR Factorization Solvers
Computers Math. Applic. Vol. 31, No. 4/5, pp. 19-26, 1996 Pergamon Copyright~)1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0898-1221/96 $15.00 + 0.00 0898-122 ! (95)00212-X Massively Parallel Poisson and QR Factorization Solvers M. LUCK£ Institute for Control Theory and Robotics, Slovak Academy of Sciences DdbravskA cesta 9, 842 37 Bratislava, Slovak Republik utrrluck@savba, sk M. VAJTERSIC Institute of Informatics, Slovak Academy of Sciences DdbravskA cesta 9, 840 00 Bratislava, P.O. Box 56, Slovak Republic kaifmava©savba, sk E. VIKTORINOVA Institute for Control Theoryand Robotics, Slovak Academy of Sciences DdbravskA cesta 9, 842 37 Bratislava, Slovak Republik utrrevka@savba, sk Abstract--The paper brings a massively parallel Poisson solver for rectangle domain and parallel algorithms for computation of QR factorization of a dense matrix A by means of Householder re- flections and Givens rotations. The computer model under consideration is a SIMD mesh-connected toroidal n x n processor array. The Dirichlet problem is replaced by its finite-difference analog on an M x N (M + 1, N are powers of two) grid. The algorithm is composed of parallel fast sine transform and cyclic odd-even reduction blocks and runs in a fully parallel fashion. Its computational complexity is O(MN log L/n2), where L = max(M + 1, N). A parallel proposal of QI~ factorization by the Householder method zeros all subdiagonal elements in each column and updates all elements of the given submatrix in parallel. For the second method with Givens rotations, the parallel scheme of the Sameh and Kuck was chosen where the disjoint rotations can be computed simultaneously. -
Accurate Eigenvalue Decomposition of Arrowhead Matrices and Applications
Accurate eigenvalue decomposition of arrowhead matrices and applications Accurate eigenvalue decomposition of arrowhead matrices and applications Nevena Jakovˇcevi´cStor FESB, University of Split joint work with Ivan Slapniˇcar and Jesse Barlow IWASEP9 June 4th, 2012. 1/31 Accurate eigenvalue decomposition of arrowhead matrices and applications Introduction We present a new algorithm (aheig) for computing eigenvalue decomposition of real symmetric arrowhead matrix. 2/31 Accurate eigenvalue decomposition of arrowhead matrices and applications Introduction We present a new algorithm (aheig) for computing eigenvalue decomposition of real symmetric arrowhead matrix. Under certain conditions, the aheig algorithm computes all eigenvalues and all components of corresponding eigenvectors with high rel. acc. in O(n2) operations. 2/31 Accurate eigenvalue decomposition of arrowhead matrices and applications Introduction We present a new algorithm (aheig) for computing eigenvalue decomposition of real symmetric arrowhead matrix. Under certain conditions, the aheig algorithm computes all eigenvalues and all components of corresponding eigenvectors with high rel. acc. in O(n2) operations. 2/31 Accurate eigenvalue decomposition of arrowhead matrices and applications Introduction We present a new algorithm (aheig) for computing eigenvalue decomposition of real symmetric arrowhead matrix. Under certain conditions, the aheig algorithm computes all eigenvalues and all components of corresponding eigenvectors with high rel. acc. in O(n2) operations. The algorithm is based on shift-and-invert technique and limited finite O(n) use of double precision arithmetic when necessary. 2/31 Accurate eigenvalue decomposition of arrowhead matrices and applications Introduction We present a new algorithm (aheig) for computing eigenvalue decomposition of real symmetric arrowhead matrix. Under certain conditions, the aheig algorithm computes all eigenvalues and all components of corresponding eigenvectors with high rel. -
High Performance Selected Inversion Methods for Sparse Matrices
High performance selected inversion methods for sparse matrices Direct and stochastic approaches to selected inversion Doctoral Dissertation submitted to the Faculty of Informatics of the Università della Svizzera italiana in partial fulfillment of the requirements for the degree of Doctor of Philosophy presented by Fabio Verbosio under the supervision of Olaf Schenk February 2019 Dissertation Committee Illia Horenko Università della Svizzera italiana, Switzerland Igor Pivkin Università della Svizzera italiana, Switzerland Matthias Bollhöfer Technische Universität Braunschweig, Germany Laura Grigori INRIA Paris, France Dissertation accepted on 25 February 2019 Research Advisor PhD Program Director Olaf Schenk Walter Binder i I certify that except where due acknowledgement has been given, the work presented in this thesis is that of the author alone; the work has not been sub- mitted previously, in whole or in part, to qualify for any other academic award; and the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research program. Fabio Verbosio Lugano, 25 February 2019 ii To my whole family. In its broadest sense. iii iv Le conoscenze matematiche sono proposizioni costruite dal nostro intelletto in modo da funzionare sempre come vere, o perché sono innate o perché la matematica è stata inventata prima delle altre scienze. E la biblioteca è stata costruita da una mente umana che pensa in modo matematico, perché senza matematica non fai labirinti. Umberto Eco, “Il nome della rosa” v vi Abstract The explicit evaluation of selected entries of the inverse of a given sparse ma- trix is an important process in various application fields and is gaining visibility in recent years. -
A Generalized Eigenvalue Algorithm for Tridiagonal Matrix Pencils Based on a Nonautonomous Discrete Integrable System
A generalized eigenvalue algorithm for tridiagonal matrix pencils based on a nonautonomous discrete integrable system Kazuki Maedaa, , Satoshi Tsujimotoa ∗ aDepartment of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan Abstract A generalized eigenvalue algorithm for tridiagonal matrix pencils is presented. The algorithm appears as the time evolution equation of a nonautonomous discrete integrable system associated with a polynomial sequence which has some orthogonality on the support set of the zeros of the characteristic polynomial for a tridiagonal matrix pencil. The convergence of the algorithm is discussed by using the solution to the initial value problem for the corresponding discrete integrable system. Keywords: generalized eigenvalue problem, nonautonomous discrete integrable system, RII chain, dqds algorithm, orthogonal polynomials 2010 MSC: 37K10, 37K40, 42C05, 65F15 1. Introduction Applications of discrete integrable systems to numerical algorithms are important and fascinating top- ics. Since the end of the twentieth century, a number of relationships between classical numerical algorithms and integrable systems have been studied (see the review papers [1–3]). On this basis, new algorithms based on discrete integrable systems have been developed: (i) singular value algorithms for bidiagonal matrices based on the discrete Lotka–Volterra equation [4, 5], (ii) Pade´ approximation algorithms based on the dis- crete relativistic Toda lattice [6] and the discrete Schur flow [7], (iii) eigenvalue algorithms for band matrices based on the discrete hungry Lotka–Volterra equation [8] and the nonautonomous discrete hungry Toda lattice [9], and (iv) algorithms for computing D-optimal designs based on the nonautonomous discrete Toda (nd-Toda) lattice [10] and the discrete modified KdV equation [11]. -
Analysis of a Fast Hankel Eigenvalue Algorithm
Analysis of a Fast Hankel Eigenvalue Algorithm a b Franklin T Luk and Sanzheng Qiao a Department of Computer Science Rensselaer Polytechnic Institute Troy New York USA b Department of Computing and Software McMaster University Hamilton Ontario LS L Canada ABSTRACT This pap er analyzes the imp ortant steps of an O n log n algorithm for nding the eigenvalues of a complex Hankel matrix The three key steps are a Lanczostype tridiagonalization algorithm a fast FFTbased Hankel matrixvector pro duct pro cedure and a QR eigenvalue metho d based on complexorthogonal transformations In this pap er we present an error analysis of the three steps as well as results from numerical exp eriments Keywords Hankel matrix eigenvalue decomp osition Lanczos tridiagonalization Hankel matrixvector multipli cation complexorthogonal transformations error analysis INTRODUCTION The eigenvalue decomp osition of a structured matrix has imp ortant applications in signal pro cessing In this pap er nn we consider a complex Hankel matrix H C h h h h n n h h h h B C n n B C B C H B C A h h h h n n n n h h h h n n n n The authors prop osed a fast algorithm for nding the eigenvalues of H The key step is a fast Lanczos tridiago nalization algorithm which employs a fast Hankel matrixvector multiplication based on the Fast Fourier transform FFT Then the algorithm p erforms a QRlike pro cedure using the complexorthogonal transformations in the di agonalization to nd the eigenvalues In this pap er we present an error analysis and discuss -
Introduction to Mathematical Programming
Introduction to Mathematical Programming Ming Zhong Lecture 24 October 29, 2018 Ming Zhong (JHU) AMS Fall 2018 1 / 18 Singular Value Decomposition (SVD) Table of Contents 1 Singular Value Decomposition (SVD) Ming Zhong (JHU) AMS Fall 2018 2 / 18 Singular Value Decomposition (SVD) Matrix Decomposition n×n We have discussed several decomposition techniques (for A 2 R ), A = LU when A is non-singular (Gaussian elimination). A = LL> when A is symmetric positive definite (Cholesky). A = QR for any real square matrix (unique when A is non-singular). A = PDP−1 if A has n linearly independent eigen-vectors). A = PJP−1 for any real square matrix. We are now ready to discuss the Singular Value Decomposition (SVD), A = UΣV∗; m×n m×m n×n m×n for any A 2 C , U 2 C , V 2 C , and Σ 2 R . Ming Zhong (JHU) AMS Fall 2018 3 / 18 Singular Value Decomposition (SVD) Some Brief History Going back in time, It was originally developed by differential geometers, equivalence of bi-linear forms by independent orthogonal transformation. Eugenio Beltrami in 1873, independently Camille Jordan in 1874, for bi-linear forms. James Joseph Sylvester in 1889 did SVD for real square matrices, independently; singular values = canonical multipliers of the matrix. Autonne in 1915 did SVD via polar decomposition. Carl Eckart and Gale Young in 1936 did the proof of SVD of rectangular and complex matrices, as a generalization of the principal axis transformaton of the Hermitian matrices. Erhard Schmidt in 1907 defined SVD for integral operators. Emile´ Picard in 1910 is the first to call the numbers singular values. -
Inexact and Nonlinear Extensions of the FEAST Eigenvalue Algorithm
University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses October 2018 Inexact and Nonlinear Extensions of the FEAST Eigenvalue Algorithm Brendan E. Gavin University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/dissertations_2 Part of the Numerical Analysis and Computation Commons, and the Numerical Analysis and Scientific Computing Commons Recommended Citation Gavin, Brendan E., "Inexact and Nonlinear Extensions of the FEAST Eigenvalue Algorithm" (2018). Doctoral Dissertations. 1341. https://doi.org/10.7275/12360224 https://scholarworks.umass.edu/dissertations_2/1341 This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. INEXACT AND NONLINEAR EXTENSIONS OF THE FEAST EIGENVALUE ALGORITHM A Dissertation Presented by BRENDAN GAVIN Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY September 2018 Electrical and Computer Engineering c Copyright by Brendan Gavin 2018 All Rights Reserved INEXACT AND NONLINEAR EXTENSIONS OF THE FEAST EIGENVALUE ALGORITHM A Dissertation Presented by BRENDAN GAVIN Approved as to style and content by: Eric Polizzi, Chair Zlatan Aksamija, Member