Christoffel Transformations of Matrix Orthogonal Polynomials
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Orthogonal Reduction 1 the Row Echelon Form -.: Mathematical
MATH 5330: Computational Methods of Linear Algebra Lecture 9: Orthogonal Reduction Xianyi Zeng Department of Mathematical Sciences, UTEP 1 The Row Echelon Form Our target is to solve the normal equation: AtAx = Atb ; (1.1) m×n where A 2 R is arbitrary; we have shown previously that this is equivalent to the least squares problem: min jjAx−bjj : (1.2) x2Rn t n×n As A A2R is symmetric positive semi-definite, we can try to compute the Cholesky decom- t t n×n position such that A A = L L for some lower-triangular matrix L 2 R . One problem with this approach is that we're not fully exploring our information, particularly in Cholesky decomposition we treat AtA as a single entity in ignorance of the information about A itself. t m×m In particular, the structure A A motivates us to study a factorization A=QE, where Q2R m×n is orthogonal and E 2 R is to be determined. Then we may transform the normal equation to: EtEx = EtQtb ; (1.3) t m×m where the identity Q Q = Im (the identity matrix in R ) is used. This normal equation is equivalent to the least squares problem with E: t min Ex−Q b : (1.4) x2Rn Because orthogonal transformation preserves the L2-norm, (1.2) and (1.4) are equivalent to each n other. Indeed, for any x 2 R : jjAx−bjj2 = (b−Ax)t(b−Ax) = (b−QEx)t(b−QEx) = [Q(Qtb−Ex)]t[Q(Qtb−Ex)] t t t t t t t t 2 = (Q b−Ex) Q Q(Q b−Ex) = (Q b−Ex) (Q b−Ex) = Ex−Q b : Hence the target is to find an E such that (1.3) is easier to solve. -
On the Eigenvalues of Euclidean Distance Matrices
“main” — 2008/10/13 — 23:12 — page 237 — #1 Volume 27, N. 3, pp. 237–250, 2008 Copyright © 2008 SBMAC ISSN 0101-8205 www.scielo.br/cam On the eigenvalues of Euclidean distance matrices A.Y. ALFAKIH∗ Department of Mathematics and Statistics University of Windsor, Windsor, Ontario N9B 3P4, Canada E-mail: [email protected] Abstract. In this paper, the notion of equitable partitions (EP) is used to study the eigenvalues of Euclidean distance matrices (EDMs). In particular, EP is used to obtain the characteristic poly- nomials of regular EDMs and non-spherical centrally symmetric EDMs. The paper also presents methods for constructing cospectral EDMs and EDMs with exactly three distinct eigenvalues. Mathematical subject classification: 51K05, 15A18, 05C50. Key words: Euclidean distance matrices, eigenvalues, equitable partitions, characteristic poly- nomial. 1 Introduction ( ) An n ×n nonzero matrix D = di j is called a Euclidean distance matrix (EDM) 1, 2,..., n r if there exist points p p p in some Euclidean space < such that i j 2 , ,..., , di j = ||p − p || for all i j = 1 n where || || denotes the Euclidean norm. i , ,..., Let p , i ∈ N = {1 2 n}, be the set of points that generate an EDM π π ( , ,..., ) D. An m-partition of D is an ordered sequence = N1 N2 Nm of ,..., nonempty disjoint subsets of N whose union is N. The subsets N1 Nm are called the cells of the partition. The n-partition of D where each cell consists #760/08. Received: 07/IV/08. Accepted: 17/VI/08. ∗Research supported by the Natural Sciences and Engineering Research Council of Canada and MITACS. -
4.1 RANK of a MATRIX Rank List Given Matrix M, the Following Are Equal
page 1 of Section 4.1 CHAPTER 4 MATRICES CONTINUED SECTION 4.1 RANK OF A MATRIX rank list Given matrix M, the following are equal: (1) maximal number of ind cols (i.e., dim of the col space of M) (2) maximal number of ind rows (i.e., dim of the row space of M) (3) number of cols with pivots in the echelon form of M (4) number of nonzero rows in the echelon form of M You know that (1) = (3) and (2) = (4) from Section 3.1. To see that (3) = (4) just stare at some echelon forms. This one number (that all four things equal) is called the rank of M. As a special case, a zero matrix is said to have rank 0. how row ops affect rank Row ops don't change the rank because they don't change the max number of ind cols or rows. example 1 12-10 24-20 has rank 1 (maximally one ind col, by inspection) 48-40 000 []000 has rank 0 example 2 2540 0001 LetM= -2 1 -1 0 21170 To find the rank of M, use row ops R3=R1+R3 R4=-R1+R4 R2ØR3 R4=-R2+R4 2540 0630 to get the unreduced echelon form 0001 0000 Cols 1,2,4 have pivots. So the rank of M is 3. how the rank is limited by the size of the matrix IfAis7≈4then its rank is either 0 (if it's the zero matrix), 1, 2, 3 or 4. The rank can't be 5 or larger because there can't be 5 ind cols when there are only 4 cols to begin with. -
Rotation Matrix - Wikipedia, the Free Encyclopedia Page 1 of 22
Rotation matrix - Wikipedia, the free encyclopedia Page 1 of 22 Rotation matrix From Wikipedia, the free encyclopedia In linear algebra, a rotation matrix is a matrix that is used to perform a rotation in Euclidean space. For example the matrix rotates points in the xy -Cartesian plane counterclockwise through an angle θ about the origin of the Cartesian coordinate system. To perform the rotation, the position of each point must be represented by a column vector v, containing the coordinates of the point. A rotated vector is obtained by using the matrix multiplication Rv (see below for details). In two and three dimensions, rotation matrices are among the simplest algebraic descriptions of rotations, and are used extensively for computations in geometry, physics, and computer graphics. Though most applications involve rotations in two or three dimensions, rotation matrices can be defined for n-dimensional space. Rotation matrices are always square, with real entries. Algebraically, a rotation matrix in n-dimensions is a n × n special orthogonal matrix, i.e. an orthogonal matrix whose determinant is 1: . The set of all rotation matrices forms a group, known as the rotation group or the special orthogonal group. It is a subset of the orthogonal group, which includes reflections and consists of all orthogonal matrices with determinant 1 or -1, and of the special linear group, which includes all volume-preserving transformations and consists of matrices with determinant 1. Contents 1 Rotations in two dimensions 1.1 Non-standard orientation -
§9.2 Orthogonal Matrices and Similarity Transformations
n×n Thm: Suppose matrix Q 2 R is orthogonal. Then −1 T I Q is invertible with Q = Q . n T T I For any x; y 2 R ,(Q x) (Q y) = x y. n I For any x 2 R , kQ xk2 = kxk2. Ex 0 1 1 1 1 1 2 2 2 2 B C B 1 1 1 1 C B − 2 2 − 2 2 C B C T H = B C ; H H = I : B C B − 1 − 1 1 1 C B 2 2 2 2 C @ A 1 1 1 1 2 − 2 − 2 2 x9.2 Orthogonal Matrices and Similarity Transformations n×n Def: A matrix Q 2 R is said to be orthogonal if its columns n (1) (2) (n)o n q ; q ; ··· ; q form an orthonormal set in R . Ex 0 1 1 1 1 1 2 2 2 2 B C B 1 1 1 1 C B − 2 2 − 2 2 C B C T H = B C ; H H = I : B C B − 1 − 1 1 1 C B 2 2 2 2 C @ A 1 1 1 1 2 − 2 − 2 2 x9.2 Orthogonal Matrices and Similarity Transformations n×n Def: A matrix Q 2 R is said to be orthogonal if its columns n (1) (2) (n)o n q ; q ; ··· ; q form an orthonormal set in R . n×n Thm: Suppose matrix Q 2 R is orthogonal. Then −1 T I Q is invertible with Q = Q . n T T I For any x; y 2 R ,(Q x) (Q y) = x y. -
The Jordan Canonical Forms of Complex Orthogonal and Skew-Symmetric Matrices Roger A
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Linear Algebra and its Applications 302–303 (1999) 411–421 www.elsevier.com/locate/laa The Jordan Canonical Forms of complex orthogonal and skew-symmetric matrices Roger A. Horn a,∗, Dennis I. Merino b aDepartment of Mathematics, University of Utah, 155 South 1400 East, Salt Lake City, UT 84112–0090, USA bDepartment of Mathematics, Southeastern Louisiana University, Hammond, LA 70402-0687, USA Received 25 August 1999; accepted 3 September 1999 Submitted by B. Cain Dedicated to Hans Schneider Abstract We study the Jordan Canonical Forms of complex orthogonal and skew-symmetric matrices, and consider some related results. © 1999 Elsevier Science Inc. All rights reserved. Keywords: Canonical form; Complex orthogonal matrix; Complex skew-symmetric matrix 1. Introduction and notation Every square complex matrix A is similar to its transpose, AT ([2, Section 3.2.3] or [1, Chapter XI, Theorem 5]), and the similarity class of the n-by-n complex symmetric matrices is all of Mn [2, Theorem 4.4.9], the set of n-by-n complex matrices. However, other natural similarity classes of matrices are non-trivial and can be characterized by simple conditions involving the Jordan Canonical Form. For example, A is similar to its complex conjugate, A (and hence also to its T adjoint, A∗ = A ), if and only if A is similar to a real matrix [2, Theorem 4.1.7]; the Jordan Canonical Form of such a matrix can contain only Jordan blocks with real eigenvalues and pairs of Jordan blocks of the form Jk(λ) ⊕ Jk(λ) for non-real λ.We denote by Jk(λ) the standard upper triangular k-by-k Jordan block with eigenvalue ∗ Corresponding author. -
Fifth Lecture
Lecture # 3 Orthogonal Matrices and Matrix Norms We repeat the definition an orthogonal set and orthornormal set. n Definition 1 A set of k vectors fu1; u2;:::; ukg, where each ui 2 R , is said to be an orthogonal with respect to the inner product (·; ·) if (ui; uj) = 0 for i =6 j. The set is said to be orthonormal if it is orthogonal and (ui; ui) = 1 for i = 1; 2; : : : ; k The definition of an orthogonal matrix is related to the definition for vectors, but with a subtle difference. n×k Definition 2 The matrix U = (u1; u2;:::; uk) 2 R whose columns form an orthonormal set is said to be left orthogonal. If k = n, that is, U is square, then U is said to be an orthogonal matrix. Note that the columns of (left) orthogonal matrices are orthonormal, not merely orthogonal. Square complex matrices whose columns form an orthonormal set are called unitary. Example 1 Here are some common 2 × 2 orthogonal matrices ! 1 0 U = 0 1 ! p 1 −1 U = 0:5 1 1 ! 0:8 0:6 U = 0:6 −0:8 ! cos θ sin θ U = − sin θ cos θ Let x 2 Rn then k k2 T Ux 2 = (Ux) (Ux) = xT U T Ux = xT x k k2 = x 2 1 So kUxk2 = kxk2: This property is called orthogonal invariance, it is an important and useful property of the two norm and orthogonal transformations. That is, orthog- onal transformations DO NOT AFFECT the two-norm, there is no compa- rable property for the one-norm or 1-norm. -
Advanced Linear Algebra (MA251) Lecture Notes Contents
Algebra I – Advanced Linear Algebra (MA251) Lecture Notes Derek Holt and Dmitriy Rumynin year 2009 (revised at the end) Contents 1 Review of Some Linear Algebra 3 1.1 The matrix of a linear map with respect to a fixed basis . ........ 3 1.2 Changeofbasis................................... 4 2 The Jordan Canonical Form 4 2.1 Introduction.................................... 4 2.2 TheCayley-Hamiltontheorem . ... 6 2.3 Theminimalpolynomial. 7 2.4 JordanchainsandJordanblocks . .... 9 2.5 Jordan bases and the Jordan canonical form . ....... 10 2.6 The JCF when n =2and3 ............................ 11 2.7 Thegeneralcase .................................. 14 2.8 Examples ...................................... 15 2.9 Proof of Theorem 2.9 (non-examinable) . ...... 16 2.10 Applications to difference equations . ........ 17 2.11 Functions of matrices and applications to differential equations . 19 3 Bilinear Maps and Quadratic Forms 21 3.1 Bilinearmaps:definitions . 21 3.2 Bilinearmaps:changeofbasis . 22 3.3 Quadraticforms: introduction. ...... 22 3.4 Quadraticforms: definitions. ..... 25 3.5 Change of variable under the general linear group . .......... 26 3.6 Change of variable under the orthogonal group . ........ 29 3.7 Applications of quadratic forms to geometry . ......... 33 3.7.1 Reduction of the general second degree equation . ....... 33 3.7.2 The case n =2............................... 34 3.7.3 The case n =3............................... 34 1 3.8 Unitary, hermitian and normal matrices . ....... 35 3.9 Applications to quantum mechanics . ..... 41 4 Finitely Generated Abelian Groups 44 4.1 Definitions...................................... 44 4.2 Subgroups,cosetsandquotientgroups . ....... 45 4.3 Homomorphisms and the first isomorphism theorem . ....... 48 4.4 Freeabeliangroups............................... 50 4.5 Unimodular elementary row and column operations and the Smith normal formforintegralmatrices . -
Orthogonal Polynomials on the Unit Circle Associated with the Laguerre Polynomials
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 129, Number 3, Pages 873{879 S 0002-9939(00)05821-4 Article electronically published on October 11, 2000 ORTHOGONAL POLYNOMIALS ON THE UNIT CIRCLE ASSOCIATED WITH THE LAGUERRE POLYNOMIALS LI-CHIEN SHEN (Communicated by Hal L. Smith) Abstract. Using the well-known fact that the Fourier transform is unitary, we obtain a class of orthogonal polynomials on the unit circle from the Fourier transform of the Laguerre polynomials (with suitable weights attached). Some related extremal problems which arise naturally in this setting are investigated. 1. Introduction This paper deals with a class of orthogonal polynomials which arise from an application of the Fourier transform on the Laguerre polynomials. We shall briefly describe the essence of our method. Let Π+ denote the upper half plane fz : z = x + iy; y > 0g and let Z 1 H(Π+)=ff : f is analytic in Π+ and sup jf(x + yi)j2 dx < 1g: 0<y<1 −∞ It is well known that, from the Paley-Wiener Theorem [4, p. 368], the Fourier transform provides a unitary isometry between the spaces L2(0; 1)andH(Π+): Since the Laguerre polynomials form a complete orthogonal basis for L2([0; 1);xαe−x dx); the application of Fourier transform to the Laguerre polynomials (with suitable weight attached) generates a class of orthogonal rational functions which are com- plete in H(Π+); and by composition of which with the fractional linear transfor- mation (which maps Π+ conformally to the unit disc) z =(2t − i)=(2t + i); we obtain a family of polynomials which are orthogonal with respect to the weight α t j j sin 2 dt on the boundary z = 1 of the unit disc. -
Linear Algebra with Exercises B
Linear Algebra with Exercises B Fall 2017 Kyoto University Ivan Ip These notes summarize the definitions, theorems and some examples discussed in class. Please refer to the class notes and reference books for proofs and more in-depth discussions. Contents 1 Abstract Vector Spaces 1 1.1 Vector Spaces . .1 1.2 Subspaces . .3 1.3 Linearly Independent Sets . .4 1.4 Bases . .5 1.5 Dimensions . .7 1.6 Intersections, Sums and Direct Sums . .9 2 Linear Transformations and Matrices 11 2.1 Linear Transformations . 11 2.2 Injection, Surjection and Isomorphism . 13 2.3 Rank . 14 2.4 Change of Basis . 15 3 Euclidean Space 17 3.1 Inner Product . 17 3.2 Orthogonal Basis . 20 3.3 Orthogonal Projection . 21 i 3.4 Orthogonal Matrix . 24 3.5 Gram-Schmidt Process . 25 3.6 Least Square Approximation . 28 4 Eigenvectors and Eigenvalues 31 4.1 Eigenvectors . 31 4.2 Determinants . 33 4.3 Characteristic polynomial . 36 4.4 Similarity . 38 5 Diagonalization 41 5.1 Diagonalization . 41 5.2 Symmetric Matrices . 44 5.3 Minimal Polynomials . 46 5.4 Jordan Canonical Form . 48 5.5 Positive definite matrix (Optional) . 52 5.6 Singular Value Decomposition (Optional) . 54 A Complex Matrix 59 ii Introduction Real life problems are hard. Linear Algebra is easy (in the mathematical sense). We make linear approximations to real life problems, and reduce the problems to systems of linear equations where we can then use the techniques from Linear Algebra to solve for approximate solutions. Linear Algebra also gives new insights and tools to the original problems. -
Arxiv:1903.11395V3 [Math.NA] 1 Dec 2020 M
Noname manuscript No. (will be inserted by the editor) The Gauss quadrature for general linear functionals, Lanczos algorithm, and minimal partial realization Stefano Pozza · Miroslav Prani´c Received: date / Accepted: date Abstract The concept of Gauss quadrature can be generalized to approx- imate linear functionals with complex moments. Following the existing lit- erature, this survey will revisit such generalization. It is well known that the (classical) Gauss quadrature for positive definite linear functionals is connected with orthogonal polynomials, and with the (Hermitian) Lanczos algorithm. Analogously, the Gauss quadrature for linear functionals is connected with for- mal orthogonal polynomials, and with the non-Hermitian Lanczos algorithm with look-ahead strategy; moreover, it is related to the minimal partial realiza- tion problem. We will review these connections pointing out the relationships between several results established independently in related contexts. Original proofs of the Mismatch Theorem and of the Matching Moment Property are given by using the properties of formal orthogonal polynomials and the Gauss quadrature for linear functionals. Keywords Linear functionals · Matching moments · Gauss quadrature · Formal orthogonal polynomials · Minimal realization · Look-ahead Lanczos algorithm · Mismatch Theorem. 1 Introduction Let A be an N × N Hermitian positive definite matrix and v a vector so that v∗v = 1, where v∗ is the conjugate transpose of v. Consider the specific linear S. Pozza Faculty of Mathematics and Physics, Charles University, Sokolovsk´a83, 186 75 Praha 8, Czech Republic. Associated member of ISTI-CNR, Pisa, Italy, and member of INdAM- GNCS group, Italy. E-mail: [email protected]ff.cuni.cz arXiv:1903.11395v3 [math.NA] 1 Dec 2020 M. -
Orthogonal Polynomials and Cubature Formulae on Spheres and on Balls∗
SIAM J. MATH. ANAL. c 1998 Society for Industrial and Applied Mathematics Vol. 29, No. 3, pp. 779{793, May 1998 015 ORTHOGONAL POLYNOMIALS AND CUBATURE FORMULAE ON SPHERES AND ON BALLS∗ YUAN XUy Abstract. Orthogonal polynomials on the unit sphere in Rd+1 and on the unit ball in Rd are shown to be closely related to each other for symmetric weight functions. Furthermore, it is shown that a large class of cubature formulae on the unit sphere can be derived from those on the unit ball and vice versa. The results provide a new approach to study orthogonal polynomials and cubature formulae on spheres. Key words. orthogonal polynomials in several variables, on spheres, on balls, spherical har- monics, cubature formulae AMS subject classifications. 33C50, 33C55, 65D32 PII. S0036141096307357 1. Introduction. We are interested in orthogonal polynomials in several vari- ables with emphasis on those orthogonal with respect to a given measure on the unit sphere Sd in Rd+1. In contrast to orthogonal polynomials with respect to measures defined on the unit ball Bd in Rd, there have been relatively few studies on the struc- ture of orthogonal polynomials on Sd beyond the ordinary spherical harmonics which are orthogonal with respect to the surface (Lebesgue) measure (cf. [2, 3, 4, 5, 6, 8]). The classical theory of spherical harmonics is primarily based on the fact that the ordinary harmonics satisfy the Laplace equation. Recently Dunkl (cf. [2, 3, 4, 5] and the references therein) opened a way to study orthogonal polynomials on the spheres with respect to measures invariant under a finite reflection group by developing a theory of spherical harmonics analogous to the classical one.