A Panorama of Positivity
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Matrix-Valued Moment Problems
Matrix-valued moment problems A Thesis Submitted to the Faculty of Drexel University by David P. Kimsey in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics June 2011 c Copyright 2011 David P. Kimsey. All Rights Reserved. ii Acknowledgments I wish to thank my advisor Professor Hugo J. Woerdeman for being a source of mathematical and professional inspiration. Woerdeman's encouragement and support have been extremely beneficial during my undergraduate and graduate career. Next, I would like to thank Professor Robert P. Boyer for providing a very important men- toring role which guided me toward a career in mathematics. I owe many thanks to L. Richard Duffy for providing encouragement and discussions on fundamental topics. Professor Dmitry Kaliuzhnyi-Verbovetskyi participated in many helpful discussions which helped shape my understanding of several mathematical topics of interest to me. In addition, Professor Anatolii Grinshpan participated in many useful discussions gave lots of encouragement. I wish to thank my Ph.D. defense committee members: Professors Boyer, Lawrence A. Fialkow, Grinshpan, Pawel Hitczenko, and Woerde- man for pointing out any gaps in my understanding as well as typographical mistakes in my thesis. Finally, I wish to acknowledge funding for my research assistantship which was provided by the National Science Foundation, via the grant DMS-0901628. iii Table of Contents Abstract ................................................................................ iv 1. INTRODUCTION ................................................................ -
Tight Frames and Their Symmetries
Technical Report 9 December 2003 Tight Frames and their Symmetries Richard Vale, Shayne Waldron Department of Mathematics, University of Auckland, Private Bag 92019, Auckland, New Zealand e–mail: [email protected] (http:www.math.auckland.ac.nz/˜waldron) e–mail: [email protected] ABSTRACT The aim of this paper is to investigate symmetry properties of tight frames, with a view to constructing tight frames of orthogonal polynomials in several variables which share the symmetries of the weight function, and other similar applications. This is achieved by using representation theory to give methods for constructing tight frames as orbits of groups of unitary transformations acting on a given finite-dimensional Hilbert space. Along the way, we show that a tight frame is determined by its Gram matrix and discuss how the symmetries of a tight frame are related to its Gram matrix. We also give a complete classification of those tight frames which arise as orbits of an abelian group of symmetries. Key Words: Tight frames, isometric tight frames, Gram matrix, multivariate orthogonal polynomials, symmetry groups, harmonic frames, representation theory, wavelets AMS (MOS) Subject Classifications: primary 05B20, 33C50, 20C15, 42C15, sec- ondary 52B15, 42C40 0 1. Introduction u1 u2 u3 2 The three equally spaced unit vectors u1, u2, u3 in IR provide the following redundant representation 2 3 f = f, u u , f IR2, (1.1) 3 h ji j ∀ ∈ j=1 X which is the simplest example of a tight frame. Such representations arose in the study of nonharmonic Fourier series in L2(IR) (see Duffin and Schaeffer [DS52]) and have recently been used extensively in the theory of wavelets (see, e.g., Daubechies [D92]). -
Polynomials and Hankel Matrices
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Polynomials and Hankel Matrices Miroslav Fiedler Czechoslovak Academy of Sciences Institute of Mathematics iitnci 25 115 67 Praha 1, Czechoslovakia Submitted by V. Ptak ABSTRACT Compatibility of a Hankel n X n matrix W and a polynomial f of degree m, m < n, is defined. If m = n, compatibility means that HC’ = CfH where Cf is the companion matrix of f With a suitable generalization of Cr, this theorem is gener- alized to the case that m < n. INTRODUCTION By a Hankel matrix [S] we shall mean a square complex matrix which has, if of order n, the form ( ai +k), i, k = 0,. , n - 1. If H = (~y~+~) is a singular n X n Hankel matrix, the H-polynomial (Pi of H was defined 131 as the greatest common divisor of the determinants of all (r + 1) x (r + 1) submatrices~of the matrix where r is the rank of H. In other words, (Pi is that polynomial for which the nX(n+l)matrix I, 0 0 0 %fb) 0 i 0 0 0 1 LINEAR ALGEBRA AND ITS APPLICATIONS 66:235-248(1985) 235 ‘F’Elsevier Science Publishing Co., Inc., 1985 52 Vanderbilt Ave., New York, NY 10017 0024.3795/85/$3.30 236 MIROSLAV FIEDLER is the Smith normal form [6] of H,. It has also been shown [3] that qN is a (nonzero) polynomial of degree at most T. It is known [4] that to a nonsingular n X n Hankel matrix H = ((Y~+~)a linear pencil of polynomials of degree at most n can be assigned as follows: f(x) = fo + f,x + . -
Week 8-9. Inner Product Spaces. (Revised Version) Section 3.1 Dot Product As an Inner Product
Math 2051 W2008 Margo Kondratieva Week 8-9. Inner product spaces. (revised version) Section 3.1 Dot product as an inner product. Consider a linear (vector) space V . (Let us restrict ourselves to only real spaces that is we will not deal with complex numbers and vectors.) De¯nition 1. An inner product on V is a function which assigns a real number, denoted by < ~u;~v> to every pair of vectors ~u;~v 2 V such that (1) < ~u;~v>=< ~v; ~u> for all ~u;~v 2 V ; (2) < ~u + ~v; ~w>=< ~u;~w> + < ~v; ~w> for all ~u;~v; ~w 2 V ; (3) < k~u;~v>= k < ~u;~v> for any k 2 R and ~u;~v 2 V . (4) < ~v;~v>¸ 0 for all ~v 2 V , and < ~v;~v>= 0 only for ~v = ~0. De¯nition 2. Inner product space is a vector space equipped with an inner product. Pn It is straightforward to check that the dot product introduces by ~u ¢ ~v = j=1 ujvj is an inner product. You are advised to verify all the properties listed in the de¯nition, as an exercise. The dot product is also called Euclidian inner product. De¯nition 3. Euclidian vector space is Rn equipped with Euclidian inner product < ~u;~v>= ~u¢~v. De¯nition 4. A square matrix A is called positive de¯nite if ~vT A~v> 0 for any vector ~v 6= ~0. · ¸ 2 0 Problem 1. Show that is positive de¯nite. 0 3 Solution: Take ~v = (x; y)T . Then ~vT A~v = 2x2 + 3y2 > 0 for (x; y) 6= (0; 0). -
Inverse Problems for Hankel and Toeplitz Matrices
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Inverse Problems for Hankel and Toeplitz Matrices Georg Heinig Vniversitiit Leipzig Sektion Mathematik Leipzig 7010, Germany Submitted by Peter Lancaster ABSTRACT The problem is considered how to construct a Toeplitz or Hankel matrix A from one or two equations of the form Au = g. The general solution is described explicitly. Special cases are inverse spectral problems for Hankel and Toeplitz matrices and problems from the theory of orthogonal polynomials. INTRODUCTION When we speak of inverse problems we have in mind the following type of problems: Given vectors uj E C”, gj E C”’ (j = l,.. .,T), we ask for an m x n matrix A of a certain matrix class such that Auj = gj (j=l,...,?-). (0.1) In the present paper we deal with inverse problems with the additional condition that A is a Hankel matrix [ si +j] or a Toeplitz matrix [ ci _j]. Inverse problems for Hankel and Toeplitz matices occur, for example, in the theory of orthogonal polynomials when a measure p on the real line or the unit circle is wanted such that given polynomials are orthogonal with respect to this measure. The moment matrix of p is just the solution of a certain inverse problem and is Hankel (in the real line case) or Toeplitz (in the unit circle case); here the gj are unit vectors. LINEAR ALGEBRA AND ITS APPLICATIONS 165:1-23 (1992) 1 0 Elsevier Science Publishing Co., Inc., 1992 655 Avenue of tbe Americas, New York, NY 10010 0024-3795/92/$5.00 2 GEORG HEINIG Inverse problems for Toeplitz matrices were considered for the first time in the paper [lo]of M. -
Gram Matrix and Orthogonality in Frames 1
U.P.B. Sci. Bull., Series A, Vol. 80, Iss. 1, 2018 ISSN 1223-7027 GRAM MATRIX AND ORTHOGONALITY IN FRAMES Abolhassan FEREYDOONI1 and Elnaz OSGOOEI 2 In this paper, we aim at introducing a criterion that determines if f figi2I is a Bessel sequence, a frame or a Riesz sequence or not any of these, based on the norms and the inner products of the elements in f figi2I. In the cases of Riesz and Bessel sequences, we introduced a criterion but in the case of a frame, we did not find any answers. This criterion will be shown by K(f figi2I). Using the criterion introduced, some interesting extensions of orthogonality will be presented. Keywords: Frames, Bessel sequences, Orthonormal basis, Riesz bases, Gram matrix MSC2010: Primary 42C15, 47A05. 1. Preliminaries Frames are generalizations of orthonormal bases, but, more than orthonormal bases, they have shown their ability and stability in the representation of functions [1, 4, 10, 11]. The frames have been deeply studied from an abstract point of view. The results of such studies have been used in concrete frames such as Gabor and Wavelet frames which are very important from a practical point of view [2, 9, 5, 8]. An orthonormal set feng in a Hilbert space is characterized by a simple relation hem;eni = dm;n: In the other words, the Gram matrix is the identity matrix. Moreover, feng is an orthonor- mal basis if spanfeng = H. But, for frames the situation is more complicated; i.e., the Gram Matrix has no such a simple form. -
Uniqueness of Low-Rank Matrix Completion by Rigidity Theory
UNIQUENESS OF LOW-RANK MATRIX COMPLETION BY RIGIDITY THEORY AMIT SINGER∗ AND MIHAI CUCURINGU† Abstract. The problem of completing a low-rank matrix from a subset of its entries is often encountered in the analysis of incomplete data sets exhibiting an underlying factor model with applications in collaborative filtering, computer vision and control. Most recent work had been focused on constructing efficient algorithms for exact or approximate recovery of the missing matrix entries and proving lower bounds for the number of known entries that guarantee a successful recovery with high probability. A related problem from both the mathematical and algorithmic point of view is the distance geometry problem of realizing points in a Euclidean space from a given subset of their pairwise distances. Rigidity theory answers basic questions regarding the uniqueness of the realization satisfying a given partial set of distances. We observe that basic ideas and tools of rigidity theory can be adapted to determine uniqueness of low-rank matrix completion, where inner products play the role that distances play in rigidity theory. This observation leads to efficient randomized algorithms for testing necessary and sufficient conditions for local completion and for testing sufficient conditions for global completion. Crucial to our analysis is a new matrix, which we call the completion matrix, that serves as the analogue of the rigidity matrix. Key words. Low rank matrices, missing values, rigidity theory, iterative methods, collaborative filtering. AMS subject classifications. 05C10, 05C75, 15A48 1. Introduction. Can the missing entries of an incomplete real valued matrix be recovered? Clearly, a matrix can be completed in an infinite number of ways by replacing the missing entries with arbitrary values. -
Inner Products and Norms (Part III)
Inner Products and Norms (part III) Prof. Dan A. Simovici UMB 1 / 74 Outline 1 Approximating Subspaces 2 Gram Matrices 3 The Gram-Schmidt Orthogonalization Algorithm 4 QR Decomposition of Matrices 5 Gram-Schmidt Algorithm in R 2 / 74 Approximating Subspaces Definition A subspace T of a inner product linear space is an approximating subspace if for every x 2 L there is a unique element in T that is closest to x. Theorem Let T be a subspace in the inner product space L. If x 2 L and t 2 T , then x − t 2 T ? if and only if t is the unique element of T closest to x. 3 / 74 Approximating Subspaces Proof Suppose that x − t 2 T ?. Then, for any u 2 T we have k x − u k2=k (x − t) + (t − u) k2=k x − t k2 + k t − u k2; by observing that x − t 2 T ? and t − u 2 T and applying Pythagora's 2 2 Theorem to x − t and t − u. Therefore, we have k x − u k >k x − t k , so t is the unique element of T closest to x. 4 / 74 Approximating Subspaces Proof (cont'd) Conversely, suppose that t is the unique element of T closest to x and x − t 62 T ?, that is, there exists u 2 T such that (x − t; u) 6= 0. This implies, of course, that u 6= 0L. We have k x − (t + au) k2=k x − t − au k2=k x − t k2 −2(x − t; au) + jaj2 k u k2 : 2 2 Since k x − (t + au) k >k x − t k (by the definition of t), we have 2 2 1 −2(x − t; au) + jaj k u k > 0 for every a 2 F. -
Exceptional Collections in Surface-Like Categories 11
EXCEPTIONAL COLLECTIONS IN SURFACE-LIKE CATEGORIES ALEXANDER KUZNETSOV Abstract. We provide a categorical framework for recent results of Markus Perling on combinatorics of exceptional collections on numerically rational surfaces. Using it we simplify and generalize some of Perling’s results as well as Vial’s criterion for existence of a numerical exceptional collection. 1. Introduction A beautiful recent paper of Markus Perling [Pe] proves that any numerically exceptional collection of maximal length in the derived category of a numerically rational surface (i.e., a surface with zero irreg- ularity and geometric genus) can be transformed by mutations into an exceptional collection consisting of objects of rank 1. In this note we provide a categorical framework that allows to extend and simplify Perling’s result. For this we introduce a notion of a surface-like category. Roughly speaking, it is a triangulated cate- T num T gory whose numerical Grothendieck group K0 ( ), considered as an abelian group with a bilinear form (Euler form), behaves similarly to the numerical Grothendieck group of a smooth projective sur- face, see Definition 3.1. Of course, for any smooth projective surface X its derived category D(X) is surface-like. However, there are surface-like categories of different nature, for instance derived categories of noncommutative surfaces and of even-dimensional Calabi–Yau varieties turn out to be surface-like (Example 3.4). Also, some subcategories of surface-like categories are surface-like. Thus, the notion of a surface-like category is indeed more general. In fact, all results of this paper have a numerical nature, so instead of considering categories, we pass directly to their numerical Grothendieck groups. -
Geometry of the Pfaff Lattices 3
GEOMETRY OF THE PFAFF LATTICES YUJI KODAMA∗ AND VIRGIL U. PIERCE∗∗ Abstract. The (semi-infinite) Pfaff lattice was introduced by Adler and van Moerbeke [2] to describe the partition functions for the random matrix models of GOE and GSE type. The partition functions of those matrix models are given by the Pfaffians of certain skew-symmetric matrices called the moment matrices, and they are the τ-functions of the Pfaff lattice. In this paper, we study a finite version of the Pfaff lattice equation as a Hamiltonian system. In particular, we prove the complete integrability in the sense of Arnold-Liouville, and using a moment map, we describe the real isospectral varieties of the Pfaff lattice. The image of the moment map is a convex polytope whose vertices are identified as the fixed points of the flow generated by the Pfaff lattice. Contents 1. Introduction 2 1.1. Lie algebra splitting related to SR-factorization 2 1.2. Hamiltonian structure 3 1.3. Outline of the paper 5 2. Integrability 6 2.1. The integrals Fr,k(L) 6 2.2. The angle variables conjugate to Fr,k(L) 10 2.3. Extended Jacobian 14 3. Matrix factorization and the τ-functions 16 3.1. Moment matrix and the τ-functions 17 3.2. Foliation of the phase space by Fr,k(L) 21 3.3. Examples from the matrix models 22 4. Real solutions 23 arXiv:0705.0510v1 [nlin.SI] 3 May 2007 4.1. Skew-orthogonal polynomials 23 4.2. Fixed points of the Pfaff flows 26 4.3. -
Applications of the Wronskian and Gram Matrices of {Fie”K?
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Applications of the Wronskian and Gram Matrices of {fie”k? Robert E. Hartwig Mathematics Department North Carolina State University Raleigh, North Carolina 27650 Submitted by S. Barn&t ABSTRACT A review is made of some of the fundamental properties of the sequence of functions {t’b’}, k=l,..., s, i=O ,..., m,_,, with distinct X,. In particular it is shown how the Wronskian and Gram matrices of this sequence of functions appear naturally in such fields as spectral matrix theory, controllability, and Lyapunov stability theory. 1. INTRODUCTION Let #(A) = II;,,< A-hk)“‘k be a complex polynomial with distinct roots x 1,.. ., A,, and let m=ml+ . +m,. It is well known [9] that the functions {tieXk’}, k=l,..., s, i=O,l,..., mkpl, form a fundamental (i.e., linearly inde- pendent) solution set to the differential equation m)Y(t)=O> (1) where D = $(.). It is the purpose of this review to illustrate some of the important properties of this independent solution set. In particular we shall show that both the Wronskian matrix and the Gram matrix play a dominant role in certain applications of matrix theory, such as spectral theory, controllability, and the Lyapunov stability theory. Few of the results in this paper are new; however, the proofs we give are novel and shed some new light on how the various concepts are interrelated, and on why certain results work the way they do. LINEAR ALGEBRA ANDITSAPPLlCATIONS43:229-241(1982) 229 C Elsevier Science Publishing Co., Inc., 1982 52 Vanderbilt Ave., New York, NY 10017 0024.3795/82/020229 + 13$02.50 230 ROBERT E. -
Pre-Publication Accepted Manuscript
Peter Forrester, Shi-Hao Li Classical discrete symplectic ensembles on the linear and exponential lattice: skew orthogonal polynomials and correlation functions Transactions of the American Mathematical Society DOI: 10.1090/tran/7957 Accepted Manuscript This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. It has not been copyedited, proofread, or finalized by AMS Production staff. Once the accepted manuscript has been copyedited, proofread, and finalized by AMS Production staff, the article will be published in electronic form as a \Recently Published Article" before being placed in an issue. That electronically published article will become the Version of Record. This preliminary version is available to AMS members prior to publication of the Version of Record, and in limited cases it is also made accessible to everyone one year after the publication date of the Version of Record. The Version of Record is accessible to everyone five years after publication in an issue. CLASSICAL DISCRETE SYMPLECTIC ENSEMBLES ON THE LINEAR AND EXPONENTIAL LATTICE: SKEW ORTHOGONAL POLYNOMIALS AND CORRELATION FUNCTIONS PETER J. FORRESTER AND SHI-HAO LI Abstract. The eigenvalue probability density function for symplectic invariant random matrix ensembles can be generalised to discrete settings involving either a linear or exponential lattice. The corresponding correlation functions can be expressed in terms of certain discrete, and q, skew orthogonal polynomials respectively. We give a theory of both of these classes of polynomials, and the correlation kernels determining the correlation functions, in the cases that the weights for the corresponding discrete unitary ensembles are classical.