MAT TRIAD 2019 Book of Abstracts
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1 Introduction
Real and Complex Hamiltonian Square Ro ots of SkewHamiltonian Matrices y z HeikeFab ender D Steven Mackey Niloufer Mackey x Hongguo Xu January DedicatedtoProfessor Ludwig Elsner on the occasion of his th birthday Abstract We present a constructive existence pro of that every real skewHamiltonian matrix W has a real Hamiltonian square ro ot The key step in this construction shows how one may bring anysuch W into a real quasiJordan canonical form via symplectic similarityWe show further that every W has innitely many real Hamiltonian square ro ots and givealower b ound on the dimension of the set of all such square ro ots Extensions to complex matrices are also presented This report is an updated version of the paper Hamiltonian squareroots of skew Hamiltonian matrices that appearedinLinear Algebra its Applicationsv pp AMS sub ject classication A A A F B Intro duction Any matrix X suchthatX A is said to b e a square ro ot of the matrix AF or general nn complex matrices A C there exists a welldevelop ed although somewhat complicated theory of matrix square ro ots and a numb er of algorithms for their eective computation Similarly for the theory and computation of real square ro ots for real matrices By contrast structured squareroot problems where b oth the matrix A Fachb ereich Mathematik und Informatik Zentrum fur Technomathematik Universitat Bremen D Bremen FRG email heikemathunibremende y Department of Mathematics Computer Science Kalamazo o College Academy Street Kalamazo o MI USA z Department of Mathematics -
Eigenvalue Perturbation Theory of Structured Real Matrices and Their Sign Characteristics Under Generic Structured Rank-One Perturbations∗
Eigenvalue perturbation theory of structured real matrices and their sign characteristics under generic structured rank-one perturbations∗ Christian Mehlx Volker Mehrmannx Andr´eC. M. Ran{ Leiba Rodmank July 9, 2014 Abstract An eigenvalue perturbation theory under rank-one perturbations is developed for classes of real matrices that are symmetric with respect to a non-degenerate bilinear form, or Hamiltonian with respect to a non-degenerate skew-symmetric form. In contrast to the case of complex matrices, the sign characteristic is a cru- cial feature of matrices in these classes. The behavior of the sign characteristic under generic rank-one perturbations is analyzed in each of these two classes of matrices. Partial results are presented, but some questions remain open. Appli- cations include boundedness and robust boundedness for solutions of structured systems of linear differential equations with respect to general perturbations as well as with respect to structured rank perturbations of the coefficients. Key Words: real J-Hamiltonian matrices, real H-symmetric matrices, indefinite inner product, perturbation analysis, generic perturbation, rank-one perturbation, T - even matrix polynomial, symmetric matrix polynomial, bounded solution of differential equations, robustly bounded solution of differential equations, invariant Lagrangian subspaces. Mathematics Subject Classification: 15A63, 15A21, 15A54, 15B57. xInstitut f¨urMathematik, TU Berlin, Straße des 17. Juni 136, D-10623 Berlin, Germany. Email: fmehl,[email protected]. {Afdeling Wiskunde, Faculteit der Exacte Wetenschappen, Vrije Universiteit Amsterdam, De Boele- laan 1081a, 1081 HV Amsterdam, The Netherlands and Unit for BMI, North West University, Potchef- stroom, South Africa. E-mail: [email protected] kCollege of William and Mary, Department of Mathematics, P.O.Box 8795, Williamsburg, VA 23187-8795, USA. -
Solving Systems of Linear Equations by Gaussian Elimination
Chapter 3 Solving Systems of Linear Equations By Gaussian Elimination 3.1 Mathematical Preliminaries In this chapter we consider the problem of computing the solution of a system of n linear equations in n unknowns. The scalar form of that system is as follows: a11x1 +a12x2 +... +... +a1nxn = b1 a x +a x +... +... +a x = b (S) 8 21 1 22 2 2n n 2 > ... ... ... ... ... <> an1x1 +an2x2 +... +... +annxn = bn > Written in matrix:> form, (S) is equivalent to: (3.1) Ax = b, where the coefficient square matrix A Rn,n, and the column vectors x, b n,1 n 2 2 R ⇠= R . Specifically, a11 a12 ... ... a1n a21 a22 ... ... a2n A = 0 1 ... ... ... ... ... B a a ... ... a C B n1 n2 nn C @ A 93 94 N. Nassif and D. Fayyad x1 b1 x2 b2 x = 0 1 and b = 0 1 . ... ... B x C B b C B n C B n C @ A @ A We assume that the basic linear algebra property for systems of linear equa- tions like (3.1) are satisfied. Specifically: Proposition 3.1. The following statements are equivalent: 1. System (3.1) has a unique solution. 2. det(A) =0. 6 3. A is invertible. In this chapter, our objective is to present the basic ideas of a linear system solver. It consists of two main procedures allowing to solve efficiently (3.1). 1. The first, referred to as Gauss elimination (or reduction) reduces (3.1) into an equivalent system of linear equations, which matrix is upper triangular. Specifically one shows in section 4 that Ax = b Ux = c, () where c Rn and U Rn,n is given by: 2 2 u11 u12 .. -
Arxiv:2009.05100V2
THE COMPLETE POSITIVITY OF SYMMETRIC TRIDIAGONAL AND PENTADIAGONAL MATRICES LEI CAO 1,2, DARIAN MCLAREN 3, AND SARAH PLOSKER 3 Abstract. We provide a decomposition that is sufficient in showing when a symmetric tridiagonal matrix A is completely positive. Our decomposition can be applied to a wide range of matrices. We give alternate proofs for a number of related results found in the literature in a simple, straightforward manner. We show that the cp-rank of any irreducible tridiagonal doubly stochastic matrix is equal to its rank. We then consider symmetric pentadiagonal matrices, proving some analogous results, and providing two different decom- positions sufficient for complete positivity. We illustrate our constructions with a number of examples. 1. Preliminaries All matrices herein will be real-valued. Let A be an n n symmetric tridiagonal matrix: × a1 b1 b1 a2 b2 . .. .. .. . A = .. .. .. . bn 3 an 2 bn 2 − − − bn 2 an 1 bn 1 − − − bn 1 an − We are often interested in the case where A is also doubly stochastic, in which case we have ai = 1 bi 1 bi for i = 1, 2,...,n, with the convention that b0 = bn = 0. It is easy to see that− if a− tridiagonal− matrix is doubly stochastic, it must be symmetric, so the additional hypothesis of symmetry can be dropped in that case. We are interested in positivity conditions for symmetric tridiagonal and pentadiagonal matrices. A stronger condition than positive semidefiniteness, known as complete positivity, arXiv:2009.05100v2 [math.CO] 10 Mar 2021 has applications in a variety of areas of study, including block designs, maximin efficiency- robust tests, modelling DNA evolution, and more [5, Chapter 2], as well as recent use in mathematical optimization and quantum information theory (see [14] and the references therein). -
Hamiltonian Matrix Representations for the Determination of Approximate Wave Functions for Molecular Resonances
Journal of Applied and Fundamental Sciences HAMILTONIAN MATRIX REPRESENTATIONS FOR THE DETERMINATION OF APPROXIMATE WAVE FUNCTIONS FOR MOLECULAR RESONANCES Robert J. Buenker Fachbereich C-Mathematik und Naturwissenschaften, Bergische Universität Wuppertal, Gaussstr. 20, D-42097 Wuppertal, Germany *For correspondence. ([email protected]) Abstract: Wave functions obtained using a standard complex Hamiltonian matrix diagonalization procedure are square integrable and therefore constitute only approximations to the corresponding resonance solutions of the Schrödinger equation. The nature of this approximation is investigated by means of explicit calculations using the above method which employ accurate diabatic potentials of the B 1Σ+ - D’ 1Σ+ vibronic resonance states of the CO molecule. It is shown that expanding the basis of complex harmonic oscillator functions gradually improves the description of the exact resonance wave functions out to ever larger internuclear distances before they take on their unwanted bound-state characteristics. The justification of the above matrix method has been based on a theorem that states that the eigenvalues of a complex-scaled Hamiltonian H (ReiΘ) are associated with the energy position and linewidth of resonance states (R is an internuclear coordinate and Θ is a real number). It is well known, however, that the results of the approximate method can be obtained directly using the unscaled Hamiltonian H (R) in real coordinates provided a particular rule is followed for the evaluation of the corresponding matrix elements. It is shown that the latter rule can itself be justified by carrying out the complex diagonalization of the Hamiltonian in real space via a product of two transformation matrices, one of which is unitary and the other is complex orthogonal, in which case only the symmetric scalar product is actually used in the evaluation of all matrix elements. -
Hamiltonian Matrices and the Algebraic Riccati Equation Seminar Presentation
Hamiltonian Matrices and the Algebraic Riccati Equation Seminar presentation by Piyapong Yuantong 7th December 2009 Technische Universit¨atChemnitz Piyapong Yuantong Hamiltonian Matrices and the Algebraic Riccati Equation 1 Hamiltonian matrices We start by introducing the square matrix J 2 R 2n×2n defined by " # O I J = n n ; (1) −In On where n×n On 2 R − zero matrix n×n In 2 R − identity matrix: Remark It is not very complicated to prove the following properties of the matrix J: i. J T = −J ii. J −1 = J T T iii. J J = I2n T T iv. J J = −I2n 2 v. J = −I2n vi. det J = 1 Definition 1 A matrix A 2 R 2n×2n is called Hamiltonian if JA is symmetric, so JA = (JA) T ) A T J + JA = 0 where J 2 R 2n×2n is from (1). We will denote { } Hn = A 2 R 2n×2n j A T J + JA = 0 the set of 2n × 2n Hamiltonian matrices. 1 Piyapong Yuantong Hamiltonian Matrices and the Algebraic Riccati Equation Proposition 1.1 The following are equivalent: a) A is a Hamiltionian matrix b) A = JS, where S = S T c) (JA) T = JA Proof a ! b A = JJ −1 A ! A = J(−J)A 2Hn A ! (J(−JA)) T J + JA = 0 ! (−JA) T J T J = −JA T J J=!I2n (−JA) T = −JA ! J(−JA) T = A If − JA = S ! A = J(−JA) = J(−JA) T ! A = JS = JS T =) S = S T a ! c A T J + JA = 0 ! A T J = −JA ()T ! (A T J) T = (−JA) T ! J T A = −(JA) T ! −JA = −(JA) T ! (JA) T = JA 2 Piyapong Yuantong Hamiltonian Matrices and the Algebraic Riccati Equation Proposition 1.2 Let A; B 2 Hn. -
Inverse Eigenvalue Problems Involving Multiple Spectra
Inverse eigenvalue problems involving multiple spectra G.M.L. Gladwell Department of Civil Engineering University of Waterloo Waterloo, Ontario, Canada N2L 3G1 [email protected] URL: http://www.civil.uwaterloo.ca/ggladwell Abstract If A Mn, its spectrum is denoted by σ(A).IfA is oscillatory (O) then σ(A∈) is positive and discrete, the submatrix A[r +1,...,n] is O and itsspectrumisdenotedbyσr(A). Itisknownthatthereisaunique symmetric tridiagonal O matrix with given, positive, strictly interlacing spectra σ0, σ1. It is shown that there is not necessarily a pentadiagonal O matrix with given, positive strictly interlacing spectra σ0, σ1, σ2, but that there is a family of such matrices with positive strictly interlacing spectra σ0, σ1. The concept of inner total positivity (ITP) is introduced, and it is shown that an ITP matrix may be reduced to ITP band form, or filled in to become TP. These reductions and filling-in procedures are used to construct ITP matrices with given multiple common spectra. 1Introduction My interest in inverse eigenvalue problems (IEP) stems from the fact that they appear in inverse vibration problems, see [7]. In these problems the matrices that appear are usually symmetric; in this paper we shall often assume that the matrices are symmetric: A Sn. ∈ If A Sn, its eigenvalues are real; we denote its spectrum by σ(A)= ∈ λ1, λ2,...,λn ,whereλ1 λ2 λn. The direct problem of finding σ{(A) from A is} well understood.≤ At≤ fi···rst≤ sight it appears that inverse eigenvalue T problems are trivial: every A Sn with spectrum σ(A) has the form Q Q ∈ ∧ where Q is orthogonal and = diag(λ1, λ2,...,λn). -
Mathematical Physics-14-Eigenvalue Problems.Nb
Eigenvalue problems Main idea and formulation in the linear algebra The word "eigenvalue" stems from the German word "Eigenwert" that can be translated into English as "Its own value" or "Inherent value". This is a value of a parameter in the equation or system of equations for which this equation has a nontriv- ial (nonzero) solution. Mathematically, the simplest formulation of the eigenvalue problem is in the linear algebra. For a given square matrix A one has to find such values of l, for which the equation (actually the system of linear equations) A.X λX (1) has a nontrivial solution for a vector (column) X. Moving the right part to the left, one obtains the equation A − λI .X 0, H L where I is the identity matrix having all diagonal elements one and nondiagonal elements zero. This matrix equation has nontrivial solutions only if its determinant is zero, Det A − λI 0. @ D This is equivalent to a Nth order algebraic equation for l, where N is the rank of the mathrix A. Thus there are N different eigenvalues ln (that can be complex), for which one can find the corresponding eigenvectors Xn. Eigenvectors are defined up to an arbitrary numerical factor, so that usually they are normalized by requiring T∗ Xn .Xn 1, where X T* is the row transposed and complex conjugate to the column X. It can be proven that eigenvectors that belong to different eigenvalues are orthogonal, so that, more generally than above, one has T∗ Xm .Xn δmn . Here dmn is the Kronecker symbol, 1, m n δ = mn µ 0, m ≠ n. -
The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices
CAUCHY –Jurnal Matematika Murni dan Aplikasi Volume 6(3) (2020), Pages 140-148 p-ISSN: 2086-0382; e-ISSN: 2477-3344 The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah1, Agustin Absari Wahyu Kuntarini 2 1,2Department of Mathematics, Faculty of Science and Technology UIN Walisongo Semarang Email: [email protected] ABSTRACT The application of centrosymmetric matrix on engineering takes their part, particularly about determinant rule. This basic rule needs a computational process for determining the appropriate algorithm. Therefore, the algorithm of the determinant kind of Hessenberg matrix is used for computing the determinant of the centrosymmetric matrix more efficiently. This paper shows the algorithm of lower Hessenberg and sparse Hessenberg matrix to construct the efficient algorithm of the determinant of a centrosymmetric matrix. Using the special structure of a centrosymmetric matrix, the algorithm of this determinant is useful for their characteristics. Key Words : Hessenberg; Determinant; Centrosymmetric INTRODUCTION One of the widely used studies in the use of centrosymmetric matrices is how to get determinant from centrosymmetric matrices. Besides this special matrix has some applications [1], it also has some properties used for determinant purpose [2]. Special characteristic centrosymmetric at this entry is evaluated at [3] resulting in the algorithm of centrosymmetric matrix at determinant. Due to sparse structure of this entry, the evaluation of the determinant matrix has simpler operations than full matrix entries. One special sparse matrix having rules on numerical analysis and arise at centrosymmetric the determinant matrix is the Hessenberg matrix. The role of Hessenberg matrix decomposition is the important role of computing the eigenvalue matrix. -
Some Bivariate Stochastic Models Arising from Group Representation Theory
Available online at www.sciencedirect.com ScienceDirect Stochastic Processes and their Applications ( ) – www.elsevier.com/locate/spa Some bivariate stochastic models arising from group representation theory Manuel D. de la Iglesiaa,∗, Pablo Románb a Instituto de Matemáticas, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., 04510, Ciudad de México, Mexico b CIEM, FaMAF, Universidad Nacional de Córdoba, Medina Allende s/n Ciudad Universitaria, Córdoba, Argentina Received 14 September 2016; accepted 31 October 2017 Available online xxxx Abstract The aim of this paper is to study some continuous-time bivariate Markov processes arising from group representation theory. The first component (level) can be either discrete (quasi-birth-and-death processes) or continuous (switching diffusion processes), while the second component (phase) will always be discrete and finite. The infinitesimal operators of these processes will be now matrix-valued (eithera block tridiagonal matrix or a matrix-valued second-order differential operator). The matrix-valued spherical functions associated to the compact symmetric pair (SU(2) × SU(2); diag SU(2)) will be eigenfunctions of these infinitesimal operators, so we can perform spectral analysis and study directly some probabilistic aspects of these processes. Among the models we study there will be rational extensions of the one-server queue and Wright–Fisher models involving only mutation effects. ⃝c 2017 Elsevier B.V. All rights reserved. MSC: 60J10; 60J60; 33C45; 42C05 Keywords: Quasi-birth-and-death processes; Switching diffusions; Matrix-valued orthogonal polynomials; Wright–Fisher models 1. Introduction It is very well known that many important results of one-dimensional stochastic processes can be obtained by using spectral methods. -
Matrix G-Strands
Technological University Dublin ARROW@TU Dublin Articles School of Mathematics 2014 Matrix G-strands Darryl Holm Imperial College London Rossen Ivanov Technological University Dublin, [email protected] Follow this and additional works at: https://arrow.tudublin.ie/scschmatart Part of the Mathematics Commons, Non-linear Dynamics Commons, and the Partial Differential Equations Commons Recommended Citation Holm, D. and Ivanov, R. Matrix G-Strands, Nonlinearity 27 (2014) 1445–1469; doi:10.1088/0951-7715/27/ 6/1445 This Article is brought to you for free and open access by the School of Mathematics at ARROW@TU Dublin. It has been accepted for inclusion in Articles by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License Funder: ERC Matrix G-Strands Darryl D. Holm1 and Rossen I. Ivanov2 9 Feb 2014, clean copy Abstract We discuss three examples in which one may extend integrable Euler–Poincar´e ODEs to integrable Euler–Poincar´ePDEs in the matrix G-Strand context. After de- scribing matrix G-Strand examples for SO(3) and SO(4) we turn our attention to SE(3) where the matrix G-Strand equations recover the exact rod theory in the con- vective representation. We then find a zero curvature representation (ZCR) of these equations and establish the conditions under which they are completely integrable. Thus, the G-Strand equations turn out to be a rich source of integrable systems. The treatment is meant to be expository and most concepts are explained in examples in the language of vectors in R3. -
CS321 Numerical Analysis
CS321 Numerical Analysis Lecture 5 System of Linear Equations Professor Jun Zhang Department of Computer Science University of Kentucky Lexington, KY 40506-0046 System of Linear Equations a11 x1 a12 x2 a1n xn b1 a21 x1 a22 x2 a2n xn b2 an1 x1 an2 x2 ann xn bn where aij are coefficients, xi are unknowns, and bi are right-hand sides. Written in a compact form is n aij x j bi , i 1,,n j1 The system can also be written in a matrix form Ax b where the matrix is a11 a12 a1n a a a A 21 22 2n an1 an2 ann and x [x , x ,, x ]T ,b [b ,b ,b ]T 1 2 n 1 2 n 2 An Upper Triangular System An upper triangular system a11 x1 a12 x2 a13 x3 a1n xn b1 a22 x2 a23 x3 a2n xn b2 a33 x3 a3n xn b3 an1,n1 xn1 an1,n xn bn1 ann xn bn is much easier to find the solution: bn xn ann from the last equation and substitute its value in other equations and repeat the process n 1 xi bi aij x j aii ji1 for i = n – 1, n – 2,…, 1 3 Karl Friedrich Gauss (April 30, 1777 – February 23, 1855) German Mathematician and Scientist 4 Gaussian Elimination Linear systems are solved by Gaussian elimination, which involves repeated procedure of multiplying a row by a number and adding it to another row to eliminate a certain variable For a particular step, this amounts to aik aij aij akj (k j n) akk aik bi bi bk akk th After this step, the variable xk, is eliminated in the (k + 1) and in the later equations The Gaussian elimination modifies a matrix into an upper triangular form such that aij = 0 for all i > j.