Algorithms for Matrix Canonical Forms
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18.06 Linear Algebra, Problem Set 2 Solutions
18.06 Problem Set 2 Solution Total: 100 points Section 2.5. Problem 24: Use Gauss-Jordan elimination on [U I] to find the upper triangular −1 U : 2 3 2 3 2 3 1 a b 1 0 0 −1 4 5 4 5 4 5 UU = I 0 1 c x1 x2 x3 = 0 1 0 : 0 0 1 0 0 1 −1 Solution (4 points): Row reduce [U I] to get [I U ] as follows (here Ri stands for the ith row): 2 3 2 3 1 a b 1 0 0 (R1 = R1 − aR2) 1 0 b − ac 1 −a 0 4 5 4 5 0 1 c 0 1 0 −! (R2 = R2 − cR2) 0 1 0 0 1 −c 0 0 1 0 0 1 0 0 1 0 0 1 ( ) 2 3 R1 = R1 − (b − ac)R3 1 0 0 1 −a ac − b −! 40 1 0 0 1 −c 5 : 0 0 1 0 0 1 Section 2.5. Problem 40: (Recommended) A is a 4 by 4 matrix with 1's on the diagonal and −1 −a; −b; −c on the diagonal above. Find A for this bidiagonal matrix. −1 Solution (12 points): Row reduce [A I] to get [I A ] as follows (here Ri stands for the ith row): 2 3 1 −a 0 0 1 0 0 0 6 7 60 1 −b 0 0 1 0 07 4 5 0 0 1 −c 0 0 1 0 0 0 0 1 0 0 0 1 2 3 (R1 = R1 + aR2) 1 0 −ab 0 1 a 0 0 6 7 (R2 = R2 + bR2) 60 1 0 −bc 0 1 b 07 −! 4 5 (R3 = R3 + cR4) 0 0 1 0 0 0 1 c 0 0 0 1 0 0 0 1 2 3 (R1 = R1 + abR3) 1 0 0 0 1 a ab abc (R = R + bcR ) 60 1 0 0 0 1 b bc 7 −! 2 2 4 6 7 : 40 0 1 0 0 0 1 c 5 0 0 0 1 0 0 0 1 Alternatively, write A = I − N. -
Theory of Angular Momentum Rotations About Different Axes Fail to Commute
153 CHAPTER 3 3.1. Rotations and Angular Momentum Commutation Relations followed by a 90° rotation about the z-axis. The net results are different, as we can see from Figure 3.1. Our first basic task is to work out quantitatively the manner in which Theory of Angular Momentum rotations about different axes fail to commute. To this end, we first recall how to represent rotations in three dimensions by 3 X 3 real, orthogonal matrices. Consider a vector V with components Vx ' VY ' and When we rotate, the three components become some other set of numbers, V;, V;, and Vz'. The old and new components are related via a 3 X 3 orthogonal matrix R: V'X Vx V'y R Vy I, (3.1.1a) V'z RRT = RTR =1, (3.1.1b) where the superscript T stands for a transpose of a matrix. It is a property of orthogonal matrices that 2 2 /2 /2 /2 vVx + V2y + Vz =IVVx + Vy + Vz (3.1.2) is automatically satisfied. This chapter is concerned with a systematic treatment of angular momen- tum and related topics. The importance of angular momentum in modern z physics can hardly be overemphasized. A thorough understanding of angu- Z z lar momentum is essential in molecular, atomic, and nuclear spectroscopy; I I angular-momentum considerations play an important role in scattering and I I collision problems as well as in bound-state problems. Furthermore, angu- I lar-momentum concepts have important generalizations-isospin in nuclear physics, SU(3), SU(2)® U(l) in particle physics, and so forth. -
On Multigrid Methods for Solving Electromagnetic Scattering Problems
On Multigrid Methods for Solving Electromagnetic Scattering Problems Dissertation zur Erlangung des akademischen Grades eines Doktor der Ingenieurwissenschaften (Dr.-Ing.) der Technischen Fakultat¨ der Christian-Albrechts-Universitat¨ zu Kiel vorgelegt von Simona Gheorghe 2005 1. Gutachter: Prof. Dr.-Ing. L. Klinkenbusch 2. Gutachter: Prof. Dr. U. van Rienen Datum der mundliche¨ Prufung:¨ 20. Jan. 2006 Contents 1 Introductory remarks 3 1.1 General introduction . 3 1.2 Maxwell’s equations . 6 1.3 Boundary conditions . 7 1.3.1 Sommerfeld’s radiation condition . 9 1.4 Scattering problem (Model Problem I) . 10 1.5 Discontinuity in a parallel-plate waveguide (Model Problem II) . 11 1.6 Absorbing-boundary conditions . 12 1.6.1 Global radiation conditions . 13 1.6.2 Local radiation conditions . 18 1.7 Summary . 19 2 Coupling of FEM-BEM 21 2.1 Introduction . 21 2.2 Finite element formulation . 21 2.2.1 Discretization . 26 2.3 Boundary-element formulation . 28 3 4 CONTENTS 2.4 Coupling . 32 3 Iterative solvers for sparse matrices 35 3.1 Introduction . 35 3.2 Classical iterative methods . 36 3.3 Krylov subspace methods . 37 3.3.1 General projection methods . 37 3.3.2 Krylov subspace methods . 39 3.4 Preconditioning . 40 3.4.1 Matrix-based preconditioners . 41 3.4.2 Operator-based preconditioners . 42 3.5 Multigrid . 43 3.5.1 Full Multigrid . 47 4 Numerical results 49 4.1 Coupling between FEM and local/global boundary conditions . 49 4.1.1 Model problem I . 50 4.1.2 Model problem II . 63 4.2 Multigrid . 64 4.2.1 Theoretical considerations regarding the classical multi- grid behavior in the case of an indefinite problem . -
[Math.RA] 19 Jun 2003 Two Linear Transformations Each Tridiagonal with Respect to an Eigenbasis of the Ot
Two linear transformations each tridiagonal with respect to an eigenbasis of the other; comments on the parameter array∗ Paul Terwilliger Abstract Let K denote a field. Let d denote a nonnegative integer and consider a sequence ∗ K p = (θi,θi , i = 0...d; ϕj , φj, j = 1...d) consisting of scalars taken from . We call p ∗ ∗ a parameter array whenever: (PA1) θi 6= θj, θi 6= θj if i 6= j, (0 ≤ i, j ≤ d); (PA2) i−1 θh−θd−h ∗ ∗ ϕi 6= 0, φi 6= 0 (1 ≤ i ≤ d); (PA3) ϕi = φ1 + (θ − θ )(θi−1 − θ ) h=0 θ0−θd i 0 d i−1 θh−θd−h ∗ ∗ (1 ≤ i ≤ d); (PA4) φi = ϕ1 + (Pθ − θ )(θd−i+1 − θ0) (1 ≤ i ≤ d); h=0 θ0−θd i 0 −1 ∗ ∗ ∗ ∗ −1 (PA5) (θi−2 − θi+1)(θi−1 − θi) P, (θi−2 − θi+1)(θi−1 − θi ) are equal and independent of i for 2 ≤ i ≤ d − 1. In [13] we showed the parameter arrays are in bijection with the isomorphism classes of Leonard systems. Using this bijection we obtain the following two characterizations of parameter arrays. Assume p satisfies PA1, PA2. Let ∗ ∗ A, B, A ,B denote the matrices in Matd+1(K) which have entries Aii = θi, Bii = θd−i, ∗ ∗ ∗ ∗ ∗ ∗ Aii = θi , Bii = θi (0 ≤ i ≤ d), Ai,i−1 = 1, Bi,i−1 = 1, Ai−1,i = ϕi, Bi−1,i = φi (1 ≤ i ≤ d), and all other entries 0. We show the following are equivalent: (i) p satisfies −1 PA3–PA5; (ii) there exists an invertible G ∈ Matd+1(K) such that G AG = B and G−1A∗G = B∗; (iii) for 0 ≤ i ≤ d the polynomial i ∗ ∗ ∗ ∗ ∗ ∗ (λ − θ0)(λ − θ1) · · · (λ − θn−1)(θi − θ0)(θi − θ1) · · · (θi − θn−1) ϕ1ϕ2 · · · ϕ nX=0 n is a scalar multiple of the polynomial i ∗ ∗ ∗ ∗ ∗ ∗ (λ − θd)(λ − θd−1) · · · (λ − θd−n+1)(θ − θ )(θ − θ ) · · · (θ − θ ) i 0 i 1 i n−1 . -
Self-Interlacing Polynomials Ii: Matrices with Self-Interlacing Spectrum
SELF-INTERLACING POLYNOMIALS II: MATRICES WITH SELF-INTERLACING SPECTRUM MIKHAIL TYAGLOV Abstract. An n × n matrix is said to have a self-interlacing spectrum if its eigenvalues λk, k = 1; : : : ; n, are distributed as follows n−1 λ1 > −λ2 > λ3 > ··· > (−1) λn > 0: A method for constructing sign definite matrices with self-interlacing spectra from totally nonnegative ones is presented. We apply this method to bidiagonal and tridiagonal matrices. In particular, we generalize a result by O. Holtz on the spectrum of real symmetric anti-bidiagonal matrices with positive nonzero entries. 1. Introduction In [5] there were introduced the so-called self-interlacing polynomials. A polynomial p(z) is called self- interlacing if all its roots are real, semple and interlacing the roots of the polynomial p(−z). It is easy to see that if λk, k = 1; : : : ; n, are the roots of a self-interlacing polynomial, then the are distributed as follows n−1 (1.1) λ1 > −λ2 > λ3 > ··· > (−1) λn > 0; or n (1.2) − λ1 > λ2 > −λ3 > ··· > (−1) λn > 0: The polynomials whose roots are distributed as in (1.1) (resp. in (1.2)) are called self-interlacing of kind I (resp. of kind II). It is clear that a polynomial p(z) is self-interlacing of kind I if, and only if, the polynomial p(−z) is self-interlacing of kind II. Thus, it is enough to study self-interlacing of kind I, since all the results for self-interlacing of kind II will be obtained automatically. Definition 1.1. An n × n matrix is said to possess a self-interlacing spectrum if its eigenvalues λk, k = 1; : : : ; n, are real, simple, are distributed as in (1.1). -
3·Singular Value Decomposition
i “book” — 2017/4/1 — 12:47 — page 91 — #93 i i i Embree – draft – 1 April 2017 3 Singular Value Decomposition · Thus far we have focused on matrix factorizations that reveal the eigenval- ues of a square matrix A n n,suchastheSchur factorization and the 2 ⇥ Jordan canonical form. Eigenvalue-based decompositions are ideal for ana- lyzing the behavior of dynamical systems likes x0(t)=Ax(t) or xk+1 = Axk. When it comes to solving linear systems of equations or tackling more general problems in data science, eigenvalue-based factorizations are often not so il- luminating. In this chapter we develop another decomposition that provides deep insight into the rank structure of a matrix, showing the way to solving all variety of linear equations and exposing optimal low-rank approximations. 3.1 Singular Value Decomposition The singular value decomposition (SVD) is remarkable factorization that writes a general rectangular matrix A m n in the form 2 ⇥ A = (unitary matrix) (diagonal matrix) (unitary matrix)⇤. ⇥ ⇥ From the unitary matrices we can extract bases for the four fundamental subspaces R(A), N(A), R(A⇤),andN(A⇤), and the diagonal matrix will reveal much about the rank structure of A. We will build up the SVD in a four-step process. For simplicity suppose that A m n with m n.(Ifm<n, apply the arguments below 2 ⇥ ≥ to A n m.) Note that A A n n is always Hermitian positive ⇤ 2 ⇥ ⇤ 2 ⇥ semidefinite. (Clearly (A⇤A)⇤ = A⇤(A⇤)⇤ = A⇤A,soA⇤A is Hermitian. For any x n,notethatx A Ax =(Ax) (Ax)= Ax 2 0,soA A is 2 ⇤ ⇤ ⇤ k k ≥ ⇤ positive semidefinite.) 91 i i i i i “book” — 2017/4/1 — 12:47 — page 92 — #94 i i i 92 Chapter 3. -
CONSTRUCTING INTEGER MATRICES with INTEGER EIGENVALUES CHRISTOPHER TOWSE,∗ Scripps College
Applied Probability Trust (25 March 2016) CONSTRUCTING INTEGER MATRICES WITH INTEGER EIGENVALUES CHRISTOPHER TOWSE,∗ Scripps College ERIC CAMPBELL,∗∗ Pomona College Abstract In spite of the proveable rarity of integer matrices with integer eigenvalues, they are commonly used as examples in introductory courses. We present a quick method for constructing such matrices starting with a given set of eigenvectors. The main feature of the method is an added level of flexibility in the choice of allowable eigenvalues. The method is also applicable to non-diagonalizable matrices, when given a basis of generalized eigenvectors. We have produced an online web tool that implements these constructions. Keywords: Integer matrices, Integer eigenvalues 2010 Mathematics Subject Classification: Primary 15A36; 15A18 Secondary 11C20 In this paper we will look at the problem of constructing a good problem. Most linear algebra and introductory ordinary differential equations classes include the topic of diagonalizing matrices: given a square matrix, finding its eigenvalues and constructing a basis of eigenvectors. In the instructional setting of such classes, concrete “toy" examples are helpful and perhaps even necessary (at least for most students). The examples that are typically given to students are, of course, integer-entry matrices with integer eigenvalues. Sometimes the eigenvalues are repeated with multipicity, sometimes they are all distinct. Oftentimes, the number 0 is avoided as an eigenvalue due to the degenerate cases it produces, particularly when the matrix in question comes from a linear system of differential equations. Yet in [10], Martin and Wong show that “Almost all integer matrices have no integer eigenvalues," let alone all integer eigenvalues. -
Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition Jean-Guillaume Dumas, Clement Pernet, Ziad Sultan
Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition Jean-Guillaume Dumas, Clement Pernet, Ziad Sultan To cite this version: Jean-Guillaume Dumas, Clement Pernet, Ziad Sultan. Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition. Journal of Symbolic Computation, Elsevier, 2017, Special issue on ISSAC’15, 83, pp.187-210. 10.1016/j.jsc.2016.11.011. hal-01251223v1 HAL Id: hal-01251223 https://hal.archives-ouvertes.fr/hal-01251223v1 Submitted on 5 Jan 2016 (v1), last revised 14 May 2018 (v2) 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. Fast Computation of the Rank Profile Matrix and the Generalized Bruhat Decomposition Jean-Guillaume Dumas Universit´eGrenoble Alpes, Laboratoire LJK, umr CNRS, BP53X, 51, av. des Math´ematiques, F38041 Grenoble, France Cl´ement Pernet Universit´eGrenoble Alpes, Laboratoire de l’Informatique du Parall´elisme, Universit´ede Lyon, France. Ziad Sultan Universit´eGrenoble Alpes, Laboratoire LJK and LIG, Inria, CNRS, Inovall´ee, 655, av. de l’Europe, F38334 St Ismier Cedex, France Abstract The row (resp. column) rank profile of a matrix describes the stair-case shape of its row (resp. -
Introduction
Introduction This dissertation is a reading of chapters 16 (Introduction to Integer Liner Programming) and 19 (Totally Unimodular Matrices: fundamental properties and examples) in the book : Theory of Linear and Integer Programming, Alexander Schrijver, John Wiley & Sons © 1986. The chapter one is a collection of basic definitions (polyhedron, polyhedral cone, polytope etc.) and the statement of the decomposition theorem of polyhedra. Chapter two is “Introduction to Integer Linear Programming”. A finite set of vectors a1 at is a Hilbert basis if each integral vector b in cone { a1 at} is a non- ,… , ,… , negative integral combination of a1 at. We shall prove Hilbert basis theorem: Each ,… , rational polyhedral cone C is generated by an integral Hilbert basis. Further, an analogue of Caratheodory’s theorem is proved: If a system n a1x β1 amx βm has no integral solution then there are 2 or less constraints ƙ ,…, ƙ among above inequalities which already have no integral solution. Chapter three contains some basic result on totally unimodular matrices. The main theorem is due to Hoffman and Kruskal: Let A be an integral matrix then A is totally unimodular if and only if for each integral vector b the polyhedron x x 0 Ax b is integral. ʜ | ƚ ; ƙ ʝ Next, seven equivalent characterization of total unimodularity are proved. These characterizations are due to Hoffman & Kruskal, Ghouila-Houri, Camion and R.E.Gomory. Basic examples of totally unimodular matrices are incidence matrices of bipartite graphs & Directed graphs and Network matrices. We prove Konig-Egervary theorem for bipartite graph. 1 Chapter 1 Preliminaries Definition 1.1: (Polyhedron) A polyhedron P is the set of points that satisfies Μ finite number of linear inequalities i.e., P = {xɛ ő | ≤ b} where (A, b) is an Μ m (n + 1) matrix. -
Sparse Matrices and Iterative Methods
Iterative Methods Sparsity Sparse Matrices and Iterative Methods K. Cooper1 1Department of Mathematics Washington State University 2018 Cooper Washington State University Introduction Iterative Methods Sparsity Iterative Methods Consider the problem of solving Ax = b, where A is n × n. Why would we use an iterative method? I Avoid direct decomposition (LU, QR, Cholesky) I Replace with iterated matrix multiplication 3 I LU is O(n ) flops. 2 I . matrix-vector multiplication is O(n )... I so if we can get convergence in e.g. log(n), iteration might be faster. Cooper Washington State University Introduction Iterative Methods Sparsity Jacobi, GS, SOR Some old methods: I Jacobi is easily parallelized. I . but converges extremely slowly. I Gauss-Seidel/SOR converge faster. I . but cannot be effectively parallelized. I Only Jacobi really takes advantage of sparsity. Cooper Washington State University Introduction Iterative Methods Sparsity Sparsity When a matrix is sparse (many more zero entries than nonzero), then typically the number of nonzero entries is O(n), so matrix-vector multiplication becomes an O(n) operation. This makes iterative methods very attractive. It does not help direct solves as much because of the problem of fill-in, but we note that there are specialized solvers to minimize fill-in. Cooper Washington State University Introduction Iterative Methods Sparsity Krylov Subspace Methods A class of methods that converge in n iterations (in exact arithmetic). We hope that they arrive at a solution that is “close enough” in fewer iterations. Often these work much better than the classic methods. They are more readily parallelized, and take full advantage of sparsity. -
Accurate Singular Values of Bidiagonal Matrices
d d Accurate Singular Values of Bidiagonal Matrices (Appeared in the SIAM J. Sci. Stat. Comput., v. 11, n. 5, pp. 873-912, 1990) James Demmel W. Kahan Courant Institute Computer Science Division 251 Mercer Str. University of California New York, NY 10012 Berkeley, CA 94720 Abstract Computing the singular values of a bidiagonal matrix is the ®nal phase of the standard algo- rithm for the singular value decomposition of a general matrix. We present a new algorithm which computes all the singular values of a bidiagonal matrix to high relative accuracy indepen- dent of their magnitudes. In contrast, the standard algorithm for bidiagonal matrices may com- pute sm all singular values with no relative accuracy at all. Numerical experiments show that the new algorithm is comparable in speed to the standard algorithm, and frequently faster. Keywords: singular value decomposition, bidiagonal matrix, QR iteration AMS(MOS) subject classi®cations: 65F20, 65G05, 65F35 1. Introduction The standard algorithm for computing the singular value decomposition (SVD ) of a gen- eral real matrix A has two phases [7]: = T 1) Compute orthogonal matrices P 11and Q such that B PAQ 11is in bidiagonal form, i.e. has nonzero entries only on its diagonal and ®rst superdiagonal. Σ= T 2) Compute orthogonal matrices P 22and Q such that PBQ 22is diagonal and nonnega- σ Σ tive. The diagonal entries i of are the singular values of A. We will take them to be σ≥σ = sorted in decreasing order: ii+112. The columns of Q QQ are the right singular vec- = tors,andthecolumnsofP PP12are the left singular vectors. -
Ratner's Work on Unipotent Flows and Impact
Ratner’s Work on Unipotent Flows and Its Impact Elon Lindenstrauss, Peter Sarnak, and Amie Wilkinson Dani above. As the name suggests, these theorems assert that the closures, as well as related features, of the orbits of such flows are very restricted (rigid). As such they provide a fundamental and powerful tool for problems connected with these flows. The brilliant techniques that Ratner in- troduced and developed in establishing this rigidity have been the blueprint for similar rigidity theorems that have been proved more recently in other contexts. We begin by describing the setup for the group of 푑×푑 matrices with real entries and determinant equal to 1 — that is, SL(푑, ℝ). An element 푔 ∈ SL(푑, ℝ) is unipotent if 푔−1 is a nilpotent matrix (we use 1 to denote the identity element in 퐺), and we will say a group 푈 < 퐺 is unipotent if every element of 푈 is unipotent. Connected unipotent subgroups of SL(푑, ℝ), in particular one-parameter unipo- Ratner presenting her rigidity theorems in a plenary tent subgroups, are basic objects in Ratner’s work. A unipo- address to the 1994 ICM, Zurich. tent group is said to be a one-parameter unipotent group if there is a surjective homomorphism defined by polyno- In this note we delve a bit more into Ratner’s rigidity theo- mials from the additive group of real numbers onto the rems for unipotent flows and highlight some of their strik- group; for instance ing applications, expanding on the outline presented by Elon Lindenstrauss is Alice Kusiel and Kurt Vorreuter professor of mathemat- 1 푡 푡2/2 1 푡 ics at The Hebrew University of Jerusalem.