Notes on Hilbert and Cauchy Matrices Miroslav Fiedler
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Extremely Accurate Solutions Using Block Decomposition and Extended Precision for Solving Very Ill-Conditioned Equations
Extremely accurate solutions using block decomposition and extended precision for solving very ill-conditioned equations †Kansa, E.J.1, Skala V.2, and Holoborodko, P.3 1Convergent Solutions, Livermore, CA 94550 USA 2Computer Science & Engineering Dept., Faculty of Applied Sciences, University of West Bohemia, University 8, CZ 301 00 Plzen, Czech Republic 3Advanpix LLC, Maison Takashima Bldg. 2F, Daimachi 10-15-201, Yokohama, Japan 221-0834 †Corresponding author: [email protected] Abstract Many authors have complained about the ill-conditioning associated the numerical solution of partial differential equations (PDEs) and integral equations (IEs) using as the continuously differentiable Gaussian and multiquadric continuously differentiable (C ) radial basis functions (RBFs). Unlike finite elements, finite difference, or finite volume methods that lave compact local support that give rise to sparse equations, the C -RBFs with simple collocation methods give rise to full, asymmetric systems of equations. Since C RBFs have adjustable constent or variable shape parameters, the resulting systems of equations that are solve on single or double precision computers can suffer from “ill-conditioning”. Condition numbers can be either the absolute or relative condition number, but in the context of linear equations, the absolute condition number will be understood. Results will be presented that demonstrates the combination of Block Gaussian elimination, arbitrary arithmetic precision, and iterative refinement can give remarkably accurate numerical salutations to large Hilbert and van der Monde equation systems. 1. Introduction An accurate definition of the condition number, , of the matrix, A, is the ratio of the largest to smallest absolute value of the singular values, { i}, obtained from the singular value decomposition (SVD) method, see [1]: (A) = maxjjminjj (1) This definition of condition number will be used in this study. -
A Study of Convergence of the PMARC Matrices Applicable to WICS Calculations
A Study of Convergence of the PMARC Matrices applicable to WICS Calculations /- by Amitabha Ghosh Department of Mechanical Engineering Rochester Institute of Technology Rochester, NY 14623 Final Report NASA Cooperative Agreement No: NCC 2-937 Presented to NASA Ames Research Center Moffett Field, CA 94035 August 31, 1997 Table of Contents Abstract ............................................ 3 Introduction .......................................... 3 Solution of Linear Systems ................................... 4 Direct Solvers .......................................... 5 Gaussian Elimination: .................................. 5 Gauss-Jordan Elimination: ................................ 5 L-U Decompostion: ................................... 6 Iterative Solvers ........................................ 6 Jacobi Method: ..................................... 7 Gauss-Seidel Method: .............................. .... 7 Successive Over-relaxation Method: .......................... 8 Conjugate Gradient Method: .............................. 9 Recent Developments: ......................... ....... 9 Computational Efficiency .................................... 10 Graphical Interpretation of Residual Correction Schemes .................... 11 Defective Matrices ............................. , ......... 15 Results and Discussion ............ ......................... 16 Concluding Remarks ...................................... 19 Acknowledgements ....................................... 19 References .......................................... -
Applied Linear Algebra
APPLIED LINEAR ALGEBRA Giorgio Picci November 24, 2015 1 Contents 1 LINEAR VECTOR SPACES AND LINEAR MAPS 10 1.1 Linear Maps and Matrices . 11 1.2 Inverse of a Linear Map . 12 1.3 Inner products and norms . 13 1.4 Inner products in coordinate spaces (1) . 14 1.5 Inner products in coordinate spaces (2) . 15 1.6 Adjoints. 16 1.7 Subspaces . 18 1.8 Image and kernel of a linear map . 19 1.9 Invariant subspaces in Rn ........................... 22 1.10 Invariant subspaces and block-diagonalization . 23 2 1.11 Eigenvalues and Eigenvectors . 24 2 SYMMETRIC MATRICES 25 2.1 Generalizations: Normal, Hermitian and Unitary matrices . 26 2.2 Change of Basis . 27 2.3 Similarity . 29 2.4 Similarity again . 30 2.5 Problems . 31 2.6 Skew-Hermitian matrices (1) . 32 2.7 Skew-Symmetric matrices (2) . 33 2.8 Square roots of positive semidefinite matrices . 36 2.9 Projections in Rn ............................... 38 2.10 Projections on general inner product spaces . 40 3 2.11 Gramians. 41 2.12 Example: Polynomial vector spaces . 42 3 LINEAR LEAST SQUARES PROBLEMS 43 3.1 Weighted Least Squares . 44 3.2 Solution by the Orthogonality Principle . 46 3.3 Matrix least-Squares Problems . 48 3.4 A problem from subspace identification . 50 3.5 Relation with Left- and Right- Inverses . 51 3.6 The Pseudoinverse . 54 3.7 The Euclidean pseudoinverse . 63 3.8 The Pseudoinverse and Orthogonal Projections . 64 3.9 Linear equations . 66 4 3.10 Unfeasible linear equations and Least Squares . 68 3.11 The Singular value decomposition (SVD) . -
Seminar VII for the Course GROUP THEORY in PHYSICS Micael Flohr
Seminar VII for the course GROUP THEORY IN PHYSICS Mic~ael Flohr The classical Lie groups 25. January 2005 MATRIX LIE GROUPS Most Lie groups one ever encouters in physics are realized as matrix Lie groups and thus as subgroups of GL(n, R) or GL(n, C). This is the group of invertibel n × n matrices with coefficients in R or C, respectively. This is a Lie group, since it forms as an open subset of the vector space of n × n matrices a manifold. Matrix multiplication is certainly a differentiable map, as is taking the inverse via Cramer’s rule. The only condition defining the open 2 subset is that the determinat must not be zero, which implies that dimKGL(n, K) = n is the same as the one of the vector space Mn(K). However, GL(n, R) is not connected, because we cannot move continuously from a matrix with determinant less than zero to one with determinant larger than zero. It is worth mentioning that gl(n, K) is the vector space of all n × n matrices over the field K, equipped with the standard commutator as Lie bracket. We can describe most other Lie groups as subgroups of GL(n, K) for either K = R or K = C. There are two ways to do so. Firstly, one can give restricting equations to the coefficients of the matrices. Secondly, one can find subgroups of the automorphisms of V =∼ Kn, which conserve a given structure on Kn. In the following, we give some examples for this: SL(n, K). -
Inertia of the Matrix [(Pi + Pj) ]
isid/ms/2013/12 October 20, 2013 http://www.isid.ac.in/estatmath/eprints r Inertia of the matrix [(pi + pj) ] Rajendra Bhatia and Tanvi Jain Indian Statistical Institute, Delhi Centre 7, SJSS Marg, New Delhi{110 016, India r INERTIA OF THE MATRIX [(pi + pj) ] RAJENDRA BHATIA* AND TANVI JAIN** Abstract. Let p1; : : : ; pn be positive real numbers. It is well r known that for every r < 0 the matrix [(pi + pj) ] is positive def- inite. Our main theorem gives a count of the number of positive and negative eigenvalues of this matrix when r > 0: Connections with some other matrices that arise in Loewner's theory of oper- ator monotone functions and in the theory of spline interpolation are discussed. 1. Introduction Let p1; p2; : : : ; pn be distinct positive real numbers. The n×n matrix 1 C = [ ] is known as the Cauchy matrix. The special case pi = i pi+pj 1 gives the Hilbert matrix H = [ i+j ]: Both matrices have been studied by several authors in diverse contexts and are much used as test matrices in numerical analysis. The Cauchy matrix is known to be positive definite. It possessesh ai ◦r 1 stronger property: for each r > 0 the entrywise power C = r (pi+pj ) is positive definite. (See [4] for a proof.) The object of this paper is to study positivity properties of the related family of matrices r Pr = [(pi + pj) ]; r ≥ 0: (1) The inertia of a Hermitian matrix A is the triple In(A) = (π(A); ζ(A); ν(A)) ; in which π(A); ζ(A) and ν(A) stand for the number of positive, zero, and negative eigenvalues of A; respectively. -
Matrix Computations
Chapter 5 Matrix Computations §5.1 Setting Up Matrix Problems §5.2 Matrix Operations §5.3 Once Again, Setting Up Matrix Problems §5.4 Recursive Matrix Operations §5.5 Distributed Memory Matrix Multiplication The next item on our agenda is the linear equation problem Ax = b. However, before we get into algorithmic details, it is important to study two simpler calculations: matrix-vector mul- tiplication and matrix-matrix multiplication. Both operations are supported in Matlab so in that sense there is “nothing to do.” However, there is much to learn by studying how these com- putations can be implemented. Matrix-vector multiplications arise during the course of solving Ax = b problems. Moreover, it is good to uplift our ability to think at the matrix-vector level before embarking on a presentation of Ax = b solvers. The act of setting up a matrix problem also deserves attention. Often, the amount of work that is required to initialize an n-by-n matrix is as much as the work required to solve for x. We pay particular attention to the common setting when each matrix entry aij is an evaluation of a continuous function f(x, y). A theme throughout this chapter is the exploitation of structure. It is frequently the case that there is a pattern among the entries in A which can be used to reduce the amount of work. The fast Fourier transform and the fast Strassen matrix multiply algorithm are presented as examples of recursion in the matrix computations. The organization of matrix-matrix multiplication on a ring of processors is also studied and gives us a nice snapshot of what algorithm development is like in a distributed memory environment. -
Fast Approximation Algorithms for Cauchy Matrices, Polynomials and Rational Functions
City University of New York (CUNY) CUNY Academic Works Computer Science Technical Reports CUNY Academic Works 2013 TR-2013011: Fast Approximation Algorithms for Cauchy Matrices, Polynomials and Rational Functions Victor Y. Pan How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_cs_tr/386 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Fast Approximation Algorithms for Cauchy Matrices, Polynomials and Rational Functions ? Victor Y. Pan Department of Mathematics and Computer Science Lehman College and the Graduate Center of the City University of New York Bronx, NY 10468 USA [email protected], home page: http://comet.lehman.cuny.edu/vpan/ Abstract. The papers [MRT05], [CGS07], [XXG12], and [XXCBa] have combined the advanced FMM techniques with transformations of matrix structures (traced back to [P90]) in order to devise numerically stable algorithms that approximate the solutions of Toeplitz, Hankel, Toeplitz- like, and Hankel-like linear systems of equations in nearly linear arith- metic time, versus classical cubic time and quadratic time of the previous advanced algorithms. We show that the power of these approximation al- gorithms can be extended to yield similar results for computations with other matrices that have displacement structure, which includes Van- dermonde and Cauchy matrices, as well as to polynomial and rational evaluation and interpolation. The resulting decrease of the running time of the known approximation algorithms is again by order of magnitude, from quadratic to nearly linear. -
An Accurate Computational Solution of Totally Positive Cauchy Linear Systems
AN ACCURATE COMPUTATIONAL SOLUTION OF TOTALLY POSITIVE CAUCHY LINEAR SYSTEMS Abdoulaye Cissé Andrew Anda Computer Science Department Computer Science Department St. Cloud State University St. Cloud State University St. Cloud, MN 56301 St. Cloud, MN 56301 [email protected] [email protected] Abstract ≈ ’ ∆ 1 The Cauchy linear systems C(x, y)V = b where C(x, y) = ∆ , x = (xi ) , y = ( yi ) , « xi − y j V = (vi ) , b = (bi ) can be solved very accurately regardless of condition number using Björck-Pereyra-type methods. We explain this phenomenon by the following facts. First, the Björck-Pereyra methods, written in matrix form, are essentially an accurate and efficient bidiagonal decomposition of the inverse of the matrix, as obtained by Neville Elimination with no pivoting. Second, each nontrivial entry of this bidiagonal decomposition is a product of two quotients of (initial) minors. We use this method to derive and implement a new method of accurately and efficiently solving totally positive Cauchy linear systems in time complexity O(n2). Keywords: Accuracy, Björck-Pereyra-type methods, Cauchy linear systems, floating point architectures, generalized Vandermonde linear systems, Neville elimination, totally positive, LDU decomposition. Note: In this paper we assume that the nodes xi and yj are pair wise distinct and C is totally positive 1 Background Definition 1.1 A Cauchy matrix is defined as: ≈ ’ ∆ 1 C(x, y) = ∆ , for i, j = 1,2,..., n (1.1) « xi − y j where the nodes xi and yj are assumed pair wise distinct. C(x,y) is totally positive (TP) if 2 2 0 < y1 < y2 < < yn−1 < yn < x1 < x2 < < xn−1 < xn . -
Contributions of Issai Schur to Analysis
Contributions of Issai Schur to Analysis Harry Dym and Victor Katsnelson∗ November 1, 2018 The name Schur is associated with many terms and concepts that are widely used in a number of diverse fields of mathematics and engineering. This survey article focuses on Schur’s work in analysis. Here too, Schur’s name is commonplace: The Schur test and Schur-Hadamard multipliers (in the study of estimates for Hermitian forms), Schur convexity, Schur complements, Schur’s results in summation theory for sequences (in particular, the fundamental Kojima-Schur theorem), the Schur-Cohn test, the Schur al- gorithm, Schur parameters and the Schur interpolation problem for functions that are holomorphic and bounded by one in the unit disk. In this survey, we shall discuss all of the above mentioned topics and then some, as well as some of the generalizations that they inspired. There are nine sections of text, each of which is devoted to a separate theme based on Schur’s work. Each of these sections has an independent bibliography. There is very little overlap. A tenth section presents a list of the papers of Schur that focus on topics that are commonly considered to be analysis. We shall begin with a review of Schur’s less familiar papers on the theory of commuting differential operators. Acknowledgement: The authors extend their thanks to Bernd Kirstein for carefully reading the manuscript and spotting a number of misprints. 1 . Permutable differential operators and fractional powers of differential operators. Let arXiv:0706.1868v1 [math.CA] 13 Jun 2007 dny dn−1y P (y)= p (x) + p (x) + + p (x)y (1.1) n dxn n−1 dxn−1 ··· 0 and dmy dm−1y Q(y)= q (x) + q (x) + + q (x)y, (1.2) m dxm m−1 dxm−1 ··· 0 ∗HD thanks Renee and Jay Weiss and VK thanks Ruth and Sylvia Shogam for endowing the chairs that support their respective research. -
Displacement Rank of the Drazin Inverse Huaian Diaoa, Yimin Weib;1, Sanzheng Qiaoc;∗;2
Available online at www.sciencedirect.com Journal of Computational and Applied Mathematics 167 (2004) 147–161 www.elsevier.com/locate/cam Displacement rank of the Drazin inverse Huaian Diaoa, Yimin Weib;1, Sanzheng Qiaoc;∗;2 aInstitute of Mathematics, Fudan University, Shanghai 200433, PR China bDepartment of Mathematics, Fudan University, Shanghai 200433, PR China cDepartment of Computing and Software, McMaster University, Hamilton, Ont., Canada L8S 4L7 Received 20 February 2003; received in revised form 4 August 2003 Abstract In this paper, we study the displacement rank of the Drazin inverse. Both Sylvester displacement and the generalized displacement are discussed. We present upper bounds for the ranks of the displacements of the Drazin inverse. The general results are applied to the group inverse of a structured matrix such as close-to- Toeplitz, generalized Cauchy, Toeplitz-plus-Hankel, and Bezoutians. c 2003 Elsevier B.V. All rights reserved. MSC: 15A09; 65F20 Keywords: Drazin inverse; Displacement; Structured matrix; Jordan canonical form 1. Introduction Displacement gives a quantitative way of identifying the structure of a matrix. Consider an n × n Toeplitz matrix t0 t−1 ··· t−n+1 . t .. .. 1 T = ; . .. .. . t−1 tn−1 ··· t1 t0 ∗ Corresponding author. Tel.: +1-905-525-9140; fax: +1-905-524-0340. E-mail addresses: [email protected] (Y. Wei), [email protected] (S. Qiao). 1 Supported by National Natural Science Foundation of China under Grant 19901006. 2 Partially supported by Natural Science and Engineering Research Council of Canada. 0377-0427/$ - see front matter c 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.cam.2003.09.050 148 H. -
Package 'Matrixcalc'
Package ‘matrixcalc’ July 28, 2021 Version 1.0-5 Date 2021-07-27 Title Collection of Functions for Matrix Calculations Author Frederick Novomestky <[email protected]> Maintainer S. Thomas Kelly <[email protected]> Depends R (>= 2.0.1) Description A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563. License GPL (>= 2) Repository CRAN Date/Publication 2021-07-28 08:00:02 UTC NeedsCompilation no R topics documented: commutation.matrix . .3 creation.matrix . .4 D.matrix . .5 direct.prod . .6 direct.sum . .7 duplication.matrix . .8 E.matrices . .9 elimination.matrix . 10 entrywise.norm . 11 1 2 R topics documented: fibonacci.matrix . 12 frobenius.matrix . 13 frobenius.norm . 14 frobenius.prod . 15 H.matrices . 17 hadamard.prod . 18 hankel.matrix . 19 hilbert.matrix . 20 hilbert.schmidt.norm . 21 inf.norm . 22 is.diagonal.matrix . 23 is.idempotent.matrix . 24 is.indefinite . 25 is.negative.definite . 26 is.negative.semi.definite . 28 is.non.singular.matrix . 29 is.positive.definite . 31 is.positive.semi.definite . 32 is.singular.matrix . 34 is.skew.symmetric.matrix . 35 is.square.matrix . 36 is.symmetric.matrix . -
Math 448/548 Cpt S 430/530 Assignment 2 Due February 9 1. Let
Math 448/548 Cpt S 430/530 Assignment 2 due February 9 1. Let A be a real n × n matrix. n (a) Let x 2 R and y = Ax. Show that for i = 1, . , n, yi (the ith component of y) satisfies n ! X jyi j ≤ jaij j kxk1: j=1 (b) Deduce that n ! X ky k ≤ max jaij j kxk 1 i 1 j=1 and n kAxk1 X max ≤ max jaij j: (1) x6=0 kxk i 1 j=1 (c) Show that equality holds in (1) by exhibiting an x for which n kAxk1 X = max jaij j: kxk i 1 j=1 (Hint: Choose an x whose entries are all ±1, with the signs care- fully chosen.) n ! X (d) Deduce that kAk = max jaij j 1 i j=1 2. Let 2 1 2 3 3 A = 4 4 5 6 5 : 7 8 9 (a) Prove by hand computation that A is singular. For example, you can show that the rows of A are linearly dependent. (b) Demonstrate that MATLAB, which uses floating-point arithmetic, does not notice that A is singular (although it does suspect that something is wrong). Use MATLAB commands like inv (inverse) and cond (condition number). 3. The Hilbert matrices are the most famous family of ill-conditioned ma- trices. The (i; j) entry of the n×n Hilbert matrix is hij = 1=(i+j −1). Using MATLAB, do the following for n = 4, 8, and 12. Generate the n×n Hilbert matrix. (MATLAB has a convenient command hilb that generates a Hilbert matrix.) Let z denote the vector of all ones, and let b = Az, where A is the Hilbert matrix.