The Singular Value Decomposition of Compact Operators on Hilbert Spaces
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On Quasi Norm Attaining Operators Between Banach Spaces
ON QUASI NORM ATTAINING OPERATORS BETWEEN BANACH SPACES GEUNSU CHOI, YUN SUNG CHOI, MINGU JUNG, AND MIGUEL MART´IN Abstract. We provide a characterization of the Radon-Nikod´ymproperty in terms of the denseness of bounded linear operators which attain their norm in a weak sense, which complement the one given by Bourgain and Huff in the 1970's. To this end, we introduce the following notion: an operator T : X ÝÑ Y between the Banach spaces X and Y is quasi norm attaining if there is a sequence pxnq of norm one elements in X such that pT xnq converges to some u P Y with }u}“}T }. Norm attaining operators in the usual (or strong) sense (i.e. operators for which there is a point in the unit ball where the norm of its image equals the norm of the operator) and also compact operators satisfy this definition. We prove that strong Radon-Nikod´ymoperators can be approximated by quasi norm attaining operators, a result which does not hold for norm attaining operators in the strong sense. This shows that this new notion of quasi norm attainment allows to characterize the Radon-Nikod´ymproperty in terms of denseness of quasi norm attaining operators for both domain and range spaces, completing thus a characterization by Bourgain and Huff in terms of norm attaining operators which is only valid for domain spaces and it is actually false for range spaces (due to a celebrated example by Gowers of 1990). A number of other related results are also included in the paper: we give some positive results on the denseness of norm attaining Lipschitz maps, norm attaining multilinear maps and norm attaining polynomials, characterize both finite dimensionality and reflexivity in terms of quasi norm attaining operators, discuss conditions to obtain that quasi norm attaining operators are actually norm attaining, study the relationship with the norm attainment of the adjoint operator and, finally, present some stability results. -
Compact Operators on Hilbert Spaces S
INTERNATIONAL JOURNAL OF MATHEMATICS AND SCIENTIFIC COMPUTING (ISSN: 2231-5330), VOL. 4, NO. 2, 2014 101 Compact Operators on Hilbert Spaces S. Nozari Abstract—In this paper, we obtain some results on compact Proof: Let T is invertible. It is well-known and easy to operators. In particular, we prove that if T is an unitary operator show that the composition of two operator which at least one on a Hilbert space H, then it is compact if and only if H has T of them be compact is also compact ([4], Th. 11.5). Therefore finite dimension. As the main theorem we prove that if be TT −1 I a hypercyclic operator on a Hilbert space, then T n (n ∈ N) is = is compact which contradicts to Lemma I.3. noncompact. Corollary II.2. If T is an invertible operator on an infinite Index Terms—Compact operator, Linear Projections, Heine- dimensional Hilbert space, then it is not compact. Borel Property. Corollary II.3. Let T be a bounded operator with finite rank MSC 2010 Codes – 46B50 on an infinite-dimensional Hilbert space H. Then T is not invertible. I. INTRODUCTION Proof: By Theorem I.4, T is compact. Now the proof is Surely, the operator theory is the heart of functional analy- completed by Theorem II.1. sis. This means that if one wish to work on functional analysis, P H he/she must study the operator theory. In operator theory, Corollary II.4. Let be a linear projection on with finite we study operators and connection between it with other rank. -
Noncommutative Uniform Algebras
Noncommutative Uniform Algebras Mati Abel and Krzysztof Jarosz Abstract. We show that a real Banach algebra A such that a2 = a 2 , for a A is a subalgebra of the algebra C (X) of continuous quaternion valued functions on a compact setk kX. ∈ H ° ° ° ° 1. Introduction A well known Hirschfeld-Zelazkoú Theorem [5](seealso[7]) states that for a complex Banach algebra A the condition (1.1) a2 = a 2 , for a A k k ∈ implies that (i) A is commutative, and further° ° implies that (ii) A must be of a very speciÞc form, namely it ° ° must be isometrically isomorphic with a uniformly closed subalgebra of CC (X). We denote here by CC (X)the complex Banach algebra of all continuous functions on a compact set X. The obvious question about the validity of the same conclusion for real Banach algebras is immediately dismissed with an obvious counterexample: the non commutative algebra H of quaternions. However it turns out that the second part (ii) above is essentially also true in the real case. We show that the condition (1.1) implies, for a real Banach algebra A,thatA is isometrically isomorphic with a subalgebra of CH (X) - the algebra of continuous quaternion valued functions on acompactsetX. That result is in fact a consequence of a theorem by Aupetit and Zemanek [1]. We will also present several related results and corollaries valid for real and complex Banach and topological algebras. To simplify the notation we assume that the algebras under consideration have units, however, like in the case of the Hirschfeld-Zelazkoú Theorem, the analogous results can be stated for none unital algebras as one can formally add aunittosuchanalgebra. -
Some Properties of the Essential Numerical Range on Banach Spaces
Pure Mathematical Sciences, Vol. 6, 2017, no. 1, 105 - 111 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/pms.2017.7912 Some Properties of the Essential Numerical Range on Banach Spaces L. N. Muhati, J. O. Bonyo and J. O. Agure Department of Pure and Applied Mathematics Maseno University, P.O. Box 333 - 40105, Maseno, Kenya Copyright c 2017 L. N. Muhati, J. O. Bonyo and J. O. Agure. This article is dis- tributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract We study the properties of the essential algebraic numerical range as well as the essential spatial numerical range for Banach space operators. Mathematics Subject Classification: 47A10; 47A05; 47A53 Keywords: Essential algebraic numerical range, Essential spatial numer- ical range, Calkin algebra 1 Introduction Let A be a complex normed algebra with a unit and let A∗ denote its dual space. For a comprehensive theory on normed algebras, we refer to [4]. We define the algebraic numerical range of an element a 2 A by V (a; A) = ff(a): f 2 A∗; f(1) = 1 = kfkg. Let X denote a complex Banach space and L(X) be the Banach algebra of all bounded linear operators acting on X. We de- note the algebraic numerical range of any T 2 L(X) by V (T; L(X)). For T 2 L(X), the spatial numerical range is defined by W (T ) = fhT x; x∗i : x 2 X; x∗ 2 X∗; kxk = 1 = kx∗k = hx; x∗ig. -
Numerical Range of Operators, Numerical Index of Banach Spaces, Lush Spaces, and Slicely Countably Determined Banach Spaces
Numerical range of operators, numerical index of Banach spaces, lush spaces, and Slicely Countably Determined Banach spaces Miguel Martín University of Granada (Spain) June 5, 2009 Preface This text is not a book and it is not in its nal form. This is going to be used as classroom notes for the talks I am going to give in the Workshop on Geometry of Banach spaces and its Applications (sponsored by Indian National Board for Higher Mathematics) to be held at the Indian Statistical Institute, Bangalore (India), on 1st - 13th June 2009. Miguel Martín Contents Preface 3 Basic notation7 1 Numerical Range of operators. Surjective isometries9 1.1 Introduction.....................................9 1.2 The exponential function. Isometries....................... 15 1.3 Finite-dimensional spaces with innitely many isometries............ 17 1.3.1 The dimension of the Lie algebra..................... 19 1.4 Surjective isometries and duality......................... 20 2 Numerical index of Banach spaces 23 2.1 Introduction..................................... 23 2.2 Computing the numerical index.......................... 23 2.3 Numerical index and duality............................ 28 2.4 Banach spaces with numerical index one..................... 32 2.5 Renorming and numerical index.......................... 34 2.6 Finite-dimensional spaces with numerical index one: asymptotic behavior.. 38 2.7 Relationship to the Daugavet property....................... 39 2.8 Smoothness, convexity and numerical index 1 .................. 43 3 Lush spaces 47 3.1 Examples of lush spaces.............................. 49 5 3.2 Lush renormings.................................. 52 3.3 Some reformulations of lushness.......................... 53 3.4 Lushness is not equivalent to numerical index 1 ................. 55 3.5 Stability results for lushness............................ 56 4 Slicely countably determined Banach spaces 59 4.1 Slicely countably determined sets........................ -
Singular Value Decomposition (SVD)
San José State University Math 253: Mathematical Methods for Data Visualization Lecture 5: Singular Value Decomposition (SVD) Dr. Guangliang Chen Outline • Matrix SVD Singular Value Decomposition (SVD) Introduction We have seen that symmetric matrices are always (orthogonally) diagonalizable. That is, for any symmetric matrix A ∈ Rn×n, there exist an orthogonal matrix Q = [q1 ... qn] and a diagonal matrix Λ = diag(λ1, . , λn), both real and square, such that A = QΛQT . We have pointed out that λi’s are the eigenvalues of A and qi’s the corresponding eigenvectors (which are orthogonal to each other and have unit norm). Thus, such a factorization is called the eigendecomposition of A, also called the spectral decomposition of A. What about general rectangular matrices? Dr. Guangliang Chen | Mathematics & Statistics, San José State University3/22 Singular Value Decomposition (SVD) Existence of the SVD for general matrices Theorem: For any matrix X ∈ Rn×d, there exist two orthogonal matrices U ∈ Rn×n, V ∈ Rd×d and a nonnegative, “diagonal” matrix Σ ∈ Rn×d (of the same size as X) such that T Xn×d = Un×nΣn×dVd×d. Remark. This is called the Singular Value Decomposition (SVD) of X: • The diagonals of Σ are called the singular values of X (often sorted in decreasing order). • The columns of U are called the left singular vectors of X. • The columns of V are called the right singular vectors of X. Dr. Guangliang Chen | Mathematics & Statistics, San José State University4/22 Singular Value Decomposition (SVD) * * b * b (n>d) b b b * b = * * = b b b * (n<d) * b * * b b Dr. -
Eigen Values and Vectors Matrices and Eigen Vectors
EIGEN VALUES AND VECTORS MATRICES AND EIGEN VECTORS 2 3 1 11 × = [2 1] [3] [ 5 ] 2 3 3 12 3 × = = 4 × [2 1] [2] [ 8 ] [2] • Scale 3 6 2 × = [2] [4] 2 3 6 24 6 × = = 4 × [2 1] [4] [16] [4] 2 EIGEN VECTOR - PROPERTIES • Eigen vectors can only be found for square matrices • Not every square matrix has eigen vectors. • Given an n x n matrix that does have eigenvectors, there are n of them for example, given a 3 x 3 matrix, there are 3 eigenvectors. • Even if we scale the vector by some amount, we still get the same multiple 3 EIGEN VECTOR - PROPERTIES • Even if we scale the vector by some amount, we still get the same multiple • Because all you’re doing is making it longer, not changing its direction. • All the eigenvectors of a matrix are perpendicular or orthogonal. • This means you can express the data in terms of these perpendicular eigenvectors. • Also, when we find eigenvectors we usually normalize them to length one. 4 EIGEN VALUES - PROPERTIES • Eigenvalues are closely related to eigenvectors. • These scale the eigenvectors • eigenvalues and eigenvectors always come in pairs. 2 3 6 24 6 × = = 4 × [2 1] [4] [16] [4] 5 SPECTRAL THEOREM Theorem: If A ∈ ℝm×n is symmetric matrix (meaning AT = A), then, there exist real numbers (the eigenvalues) λ1, …, λn and orthogonal, non-zero real vectors ϕ1, ϕ2, …, ϕn (the eigenvectors) such that for each i = 1,2,…, n : Aϕi = λiϕi 6 EXAMPLE 30 28 A = [28 30] From spectral theorem: Aϕ = λϕ 7 EXAMPLE 30 28 A = [28 30] From spectral theorem: Aϕ = λϕ ⟹ Aϕ − λIϕ = 0 (A − λI)ϕ = 0 30 − λ 28 = 0 ⟹ λ = 58 and -
Numerical Range and Functional Calculus in Hilbert Space
Numerical range and functional calculus in Hilbert space Michel Crouzeix Abstract We prove an inequality related to polynomial functions of a square matrix, involving the numerical range of the matrix. We also show extensions valid for bounded and also unbounded operators in Hilbert spaces, which allow the development of a functional calculus. Mathematical subject classification : 47A12 ; 47A25 Keywords : functional calculus, numerical range, spectral sets. 1 Introduction In this paper H denotes a complex Hilbert space equipped with the inner product . , . and h i corresponding norm v = v, v 1/2. We denote its unit sphere by Σ := v H; v = 1 . For k k h i H { ∈ k k } a bounded linear operator A (H) we use the operator norm A and denote by W (A) its ∈ L k k numerical range : A := sup A v , W (A) := Av,v ; v ΣH . k k v ΣH k k {h i ∈ } ∈ Recall that the numerical range is a convex subset of C and that its closure contains the spectrum of A. We keep the same notation in the particular case where H = Cd and A is a d d matrix. × The main aim of this article is to prove that the inequality p(A) 56 sup p(z) (1) k k≤ z W (A) | | ∈ holds for any matrix A Cd,d and any polynomial p : C C (independently of d). ∈ → It is remarkable that the completely bounded version of this inequality is also valid. More precisely, we consider now matrix-valued polynomials P : C Cm,n, i.e., for z C, P (z) = → ∈ (p (z)) is a matrix, with each entry p (.) a polynomial C C. -
Numerical Range of Square Matrices a Study in Spectral Theory
Numerical Range of Square Matrices A Study in Spectral Theory Department of Mathematics, Linköping University Erik Jonsson LiTH-MAT-EX–2019/03–SE Credits: 16 hp Level: G2 Supervisor: Göran Bergqvist, Department of Mathematics, Linköping University Examiner: Milagros Izquierdo, Department of Mathematics, Linköping University Linköping: June 2019 Abstract In this thesis, we discuss important results for the numerical range of general square matrices. Especially, we examine analytically the numerical range of complex-valued 2 × 2 matrices. Also, we investigate and discuss the Gershgorin region of general square matrices. Lastly, we examine numerically the numerical range and Gershgorin regions for different types of square matrices, both contain the spectrum of the matrix, and compare these regions, using the calculation software Maple. Keywords: numerical range, square matrix, spectrum, Gershgorin regions URL for electronic version: urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157661 Erik Jonsson, 2019. iii Sammanfattning I denna uppsats diskuterar vi viktiga resultat för numerical range av gene- rella kvadratiska matriser. Speciellt undersöker vi analytiskt numerical range av komplexvärda 2 × 2 matriser. Vi utreder och diskuterar också Gershgorin områden för generella kvadratiska matriser. Slutligen undersöker vi numeriskt numerical range och Gershgorin områden för olika typer av matriser, där båda innehåller matrisens spektrum, och jämför dessa områden genom att använda beräkningsprogrammet Maple. Nyckelord: numerical range, kvadratisk matris, spektrum, Gershgorin områden URL för elektronisk version: urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157661 Erik Jonsson, 2019. v Acknowledgements I’d like to thank my supervisor Göran Bergqvist, who has shown his great interest for linear algebra and inspired me for writing this thesis. -
An Image Problem for Compact Operators 1393
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 134, Number 5, Pages 1391–1396 S 0002-9939(05)08084-6 Article electronically published on October 7, 2005 AN IMAGE PROBLEM FOR COMPACT OPERATORS ISABELLE CHALENDAR AND JONATHAN R. PARTINGTON (Communicated by Joseph A. Ball) Abstract. Let X be a separable Banach space and (Xn)n a sequence of closed subspaces of X satisfying Xn ⊂Xn+1 for all n. We first prove the existence of a dense-range and injective compact operator K such that each KXn is a dense subset of Xn, solving a problem of Yahaghi (2004). Our second main result concerns isomorphic and dense-range injective compact mappings be- tween dense sets of linearly independent vectors, extending a result of Grivaux (2003). 1. Introduction Let X be an infinite-dimensional separable real or complex Banach space and denote by L(X ) the algebra of all bounded and linear mappings from X to X .A chain of subspaces of X is defined to be a sequence (at most countable) (Xn)n≥0 of closed subspaces of X such that X0 = {0} and Xn ⊂Xn+1 for all n ≥ 0. The identity map on X is denoted by Id. Section 2 of this paper is devoted to the construction of injective and dense- range compact operators K ∈L(X ) such that KXn is a dense subset of Xn for all n. Note that for separable Hilbert spaces, using an orthonormal basis B and diagonal compact operators relative to B, it is not difficult to construct injective, dense-range and normal compact operators such that each KXn is a dense subset of Xn. -
Singular Value Decomposition (SVD) 2 1.1 Singular Vectors
Contents 1 Singular Value Decomposition (SVD) 2 1.1 Singular Vectors . .3 1.2 Singular Value Decomposition (SVD) . .7 1.3 Best Rank k Approximations . .8 1.4 Power Method for Computing the Singular Value Decomposition . 11 1.5 Applications of Singular Value Decomposition . 16 1.5.1 Principal Component Analysis . 16 1.5.2 Clustering a Mixture of Spherical Gaussians . 16 1.5.3 An Application of SVD to a Discrete Optimization Problem . 22 1.5.4 SVD as a Compression Algorithm . 24 1.5.5 Spectral Decomposition . 24 1.5.6 Singular Vectors and ranking documents . 25 1.6 Bibliographic Notes . 27 1.7 Exercises . 28 1 1 Singular Value Decomposition (SVD) The singular value decomposition of a matrix A is the factorization of A into the product of three matrices A = UDV T where the columns of U and V are orthonormal and the matrix D is diagonal with positive real entries. The SVD is useful in many tasks. Here we mention some examples. First, in many applications, the data matrix A is close to a matrix of low rank and it is useful to find a low rank matrix which is a good approximation to the data matrix . We will show that from the singular value decomposition of A, we can get the matrix B of rank k which best approximates A; in fact we can do this for every k. Also, singular value decomposition is defined for all matrices (rectangular or square) unlike the more commonly used spectral decomposition in Linear Algebra. The reader familiar with eigenvectors and eigenvalues (we do not assume familiarity here) will also realize that we need conditions on the matrix to ensure orthogonality of eigenvectors. -
A Singularly Valuable Decomposition: the SVD of a Matrix Dan Kalman
A Singularly Valuable Decomposition: The SVD of a Matrix Dan Kalman Dan Kalman is an assistant professor at American University in Washington, DC. From 1985 to 1993 he worked as an applied mathematician in the aerospace industry. It was during that period that he first learned about the SVD and its applications. He is very happy to be teaching again and is avidly following all the discussions and presentations about the teaching of linear algebra. Every teacher of linear algebra should be familiar with the matrix singular value deco~??positiolz(or SVD). It has interesting and attractive algebraic properties, and conveys important geometrical and theoretical insights about linear transformations. The close connection between the SVD and the well-known theo1-j~of diagonalization for sylnmetric matrices makes the topic immediately accessible to linear algebra teachers and, indeed, a natural extension of what these teachers already know. At the same time, the SVD has fundamental importance in several different applications of linear algebra. Gilbert Strang was aware of these facts when he introduced the SVD in his now classical text [22, p. 1421, obselving that "it is not nearly as famous as it should be." Golub and Van Loan ascribe a central significance to the SVD in their defini- tive explication of numerical matrix methods [8, p, xivl, stating that "perhaps the most recurring theme in the book is the practical and theoretical value" of the SVD. Additional evidence of the SVD's significance is its central role in a number of re- cent papers in :Matlgenzatics ivlagazine and the Atnericalz Mathematical ilironthly; for example, [2, 3, 17, 231.