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Operator Theory in the C -Algebra Framework
Operator theory in the C∗-algebra framework S.L.Woronowicz and K.Napi´orkowski Department of Mathematical Methods in Physics Faculty of Physics Warsaw University Ho˙za74, 00-682 Warszawa Abstract Properties of operators affiliated with a C∗-algebra are studied. A functional calculus of normal elements is constructed. Representations of locally compact groups in a C∗-algebra are considered. Generaliza- tions of Stone and Nelson theorems are investigated. It is shown that the tensor product of affiliated elements is affiliated with the tensor product of corresponding algebras. 1 0 Introduction Let H be a Hilbert space and CB(H) be the algebra of all compact operators acting on H. It was pointed out in [17] that the classical theory of unbounded closed operators acting in H [8, 9, 3] is in a sense related to CB(H). It seems to be interesting to replace in this context CB(H) by any non-unital C∗- algebra. A step in this direction is done in the present paper. We shall deal with the following topics: the functional calculus of normal elements (Section 1), the representation theory of Lie groups including the Stone theorem (Sections 2,3 and 4) and the extensions of symmetric elements (Section 5). Section 6 contains elementary results related to tensor products. The perturbation theory (in the spirit of T.Kato) is not covered in this pa- per. The elementary results in this direction are contained the first author’s previous paper (cf. [17, Examples 1, 2 and 3 pp. 412–413]). To fix the notation we remind the basic definitions and results [17]. -
Functional Analysis Lecture Notes Chapter 2. Operators on Hilbert Spaces
FUNCTIONAL ANALYSIS LECTURE NOTES CHAPTER 2. OPERATORS ON HILBERT SPACES CHRISTOPHER HEIL 1. Elementary Properties and Examples First recall the basic definitions regarding operators. Definition 1.1 (Continuous and Bounded Operators). Let X, Y be normed linear spaces, and let L: X Y be a linear operator. ! (a) L is continuous at a point f X if f f in X implies Lf Lf in Y . 2 n ! n ! (b) L is continuous if it is continuous at every point, i.e., if fn f in X implies Lfn Lf in Y for every f. ! ! (c) L is bounded if there exists a finite K 0 such that ≥ f X; Lf K f : 8 2 k k ≤ k k Note that Lf is the norm of Lf in Y , while f is the norm of f in X. k k k k (d) The operator norm of L is L = sup Lf : k k kfk=1 k k (e) We let (X; Y ) denote the set of all bounded linear operators mapping X into Y , i.e., B (X; Y ) = L: X Y : L is bounded and linear : B f ! g If X = Y = X then we write (X) = (X; X). B B (f) If Y = F then we say that L is a functional. The set of all bounded linear functionals on X is the dual space of X, and is denoted X0 = (X; F) = L: X F : L is bounded and linear : B f ! g We saw in Chapter 1 that, for a linear operator, boundedness and continuity are equivalent. -
Quantum Mechanics, Schrodinger Operators and Spectral Theory
Quantum mechanics, SchrÄodingeroperators and spectral theory. Spectral theory of SchrÄodinger operators has been my original ¯eld of expertise. It is a wonderful mix of functional analy- sis, PDE and Fourier analysis. In quantum mechanics, every physical observable is described by a self-adjoint operator - an in¯nite dimensional symmetric matrix. For example, ¡¢ is a self-adjoint operator in L2(Rd) when de¯ned on an appropriate domain (Sobolev space H2). In quantum mechanics it corresponds to a free particle - just traveling in space. What does it mean, exactly? Well, in quantum mechanics, position of a particle is described by a wave 2 d function, Á(x; t) 2 L (R ): The physical meaningR of the wave function is that the probability 2 to ¯nd it in a region at time t is equal to jÁ(x; t)j dx: You can't know for sure where the particle is for sure. If initially the wave function is given by Á(x; 0) = Á0(x); the SchrÄodinger ¡i¢t equation says that its evolution is given by Á(x; t) = e Á0: But how to compute what is e¡i¢t? This is where Fourier transform comes in handy. Di®erentiation becomes multiplication on the Fourier transform side, and so Z ¡i¢t ikx¡ijkj2t ^ e Á0(x) = e Á0(k) dk; Rd R ^ ¡ikx where Á0(k) = Rd e Á0(x) dx is the Fourier transform of Á0: I omitted some ¼'s since they do not change the picture. Stationary phase methods lead to very precise estimates on free SchrÄodingerevolution. -
Operator Algebras: an Informal Overview 3
OPERATOR ALGEBRAS: AN INFORMAL OVERVIEW FERNANDO LLEDO´ Contents 1. Introduction 1 2. Operator algebras 2 2.1. What are operator algebras? 2 2.2. Differences and analogies between C*- and von Neumann algebras 3 2.3. Relevance of operator algebras 5 3. Different ways to think about operator algebras 6 3.1. Operator algebras as non-commutative spaces 6 3.2. Operator algebras as a natural universe for spectral theory 6 3.3. Von Neumann algebras as symmetry algebras 7 4. Some classical results 8 4.1. Operator algebras in functional analysis 8 4.2. Operator algebras in harmonic analysis 10 4.3. Operator algebras in quantum physics 11 References 13 Abstract. In this article we give a short and informal overview of some aspects of the theory of C*- and von Neumann algebras. We also mention some classical results and applications of these families of operator algebras. 1. Introduction arXiv:0901.0232v1 [math.OA] 2 Jan 2009 Any introduction to the theory of operator algebras, a subject that has deep interrelations with many mathematical and physical disciplines, will miss out important elements of the theory, and this introduction is no ex- ception. The purpose of this article is to give a brief and informal overview on C*- and von Neumann algebras which are the main actors of this summer school. We will also mention some of the classical results in the theory of operator algebras that have been crucial for the development of several areas in mathematics and mathematical physics. Being an overview we can not provide details. Precise definitions, statements and examples can be found in [1] and references cited therein. -
Computing the Singular Value Decomposition to High Relative Accuracy
Computing the Singular Value Decomposition to High Relative Accuracy James Demmel Department of Mathematics Department of Electrical Engineering and Computer Science University of California - Berkeley [email protected] Plamen Koev Department of Mathematics University of California - Berkeley [email protected] Structured Matrices In Operator Theory, Numerical Analysis, Control, Signal and Image Processing Boulder, Colorado June 27-July 1, 1999 Supported by NSF and DOE INTRODUCTION • High Relative Accuracy means computing the correct SIGN and LEADING DIGITS • Singular Value Decomposition (SVD): A = UΣV T where U, V are orthogonal, σ1 σ2 Σ = and σ1 ≥ σ2 ≥ . σn ≥ 0 .. . σn • GOAL: Compute all σi with high relative accuracy, even when σi σ1 • It all comes down to being able to compute determi- nants to high relative accuracy. Example: 100 by 100 Hilbert Matrix H(i, j) = 1/(i + j − 1) • Singular values range from 1 down to 10−150 • Old algorithm, New Algorithm, both in 16 digits Singular values of Hilb(100), blue = accurate, red = usual 0 10 −20 10 −40 10 −60 10 −80 10 −100 10 −120 10 −140 10 0 10 20 30 40 50 60 70 80 90 100 • D = log(cond(A)) = log(σ1/σn) (here D = 150) • Cost of Old algorithm = O(n3D2) • Cost of New algorithm = O(n3), independent of D – Run in double, not bignums as in Mathematica – New hundreds of times faster than Old • When does it work? Not for all matrices ... • Why bother? Why do we want tiny singular values accurately? 1. When they are determined accurately by the data • Hilbert: H(i, j) = 1/(i + j − 1) • Cauchy: C(i, j) = 1/(xi + yj) 2. -
Variational Calculus of Supervariables and Related Algebraic Structures1
Variational Calculus of Supervariables and Related Algebraic Structures1 Xiaoping Xu Department of Mathematics, The Hong Kong University of Science & Technology Clear Water Bay, Kowloon, Hong Kong2 Abstract We establish a formal variational calculus of supervariables, which is a combination of the bosonic theory of Gel’fand-Dikii and the fermionic theory in our earlier work. Certain interesting new algebraic structures are found in connection with Hamiltonian superoperators in terms of our theory. In particular, we find connections between Hamiltonian superoperators and Novikov- Poisson algebras that we introduced in our earlier work in order to establish a tensor theory of Novikov algebras. Furthermore, we prove that an odd linear Hamiltonian superoperator in our variational calculus induces a Lie superalgebra, which is a natural generalization of the Super-Virasoro algebra under certain conditions. 1 Introduction Formal variational calculus was introduced by Gel’fand and Dikii [GDi1-2] in studying Hamiltonian systems related to certain nonlinear partial differential equation, such as the arXiv:math/9911191v1 [math.QA] 24 Nov 1999 KdV equations. Invoking the variational derivatives, they found certain interesting Pois- son structures. Moreover, Gel’fand and Dorfman [GDo] found more connections between Hamiltonian operators and algebraic structures. Balinskii and Novikov [BN] studied sim- ilar Poisson structures from another point of view. The nature of Gel’fand and Dikii’s formal variational calculus is bosonic. In [X3], we presented a general frame of Hamiltonian superoperators and a purely fermionic formal variational calculus. Our work [X3] was based on pure algebraic analogy. In this paper, we shall present a formal variational calculus of supervariables, which is a combination 11991 Mathematical Subject Classification. -
Functional Analysis (WS 19/20), Problem Set 8 (Spectrum And
Functional Analysis (WS 19/20), Problem Set 8 (spectrum and adjoints on Hilbert spaces)1 In what follows, let H be a complex Hilbert space. Let T : H ! H be a bounded linear operator. We write T ∗ : H ! H for adjoint of T dened with hT x; yi = hx; T ∗yi: This operator exists and is uniquely determined by Riesz Representation Theorem. Spectrum of T is the set σ(T ) = fλ 2 C : T − λI does not have a bounded inverseg. Resolvent of T is the set ρ(T ) = C n σ(T ). Basic facts on adjoint operators R1. | Adjoint T ∗ exists and is uniquely determined. R2. | Adjoint T ∗ is a bounded linear operator and kT ∗k = kT k. Moreover, kT ∗T k = kT k2. R3. | Taking adjoints is an involution: (T ∗)∗ = T . R4. Adjoints commute with the sum: ∗ ∗ ∗. | (T1 + T2) = T1 + T2 ∗ ∗ R5. | For λ 2 C we have (λT ) = λ T . R6. | Let T be a bounded invertible operator. Then, (T ∗)−1 = (T −1)∗. R7. Let be bounded operators. Then, ∗ ∗ ∗. | T1;T2 (T1 T2) = T2 T1 R8. | We have relationship between kernel and image of T and T ∗: ker T ∗ = (im T )?; (ker T ∗)? = im T It will be helpful to prove that if M ⊂ H is a linear subspace, then M = M ??. Btw, this covers all previous results like if N is a nite dimensional linear subspace then N = N ?? (because N is closed). Computation of adjoints n n ∗ M1. ,Let A : R ! R be a complex matrix. Find A . 2 M2. | ,Let H = l (Z). For x = (:::; x−2; x−1; x0; x1; x2; :::) 2 H we dene the right shift operator −1 ∗ with (Rx)k = xk−1: Find kRk, R and R . -
Quantum Dynamics
Quantum Dynamics William G. Faris August 12, 1992 Contents I Hilbert Space 5 1 Introduction 7 1.1 Quantum mechanics . 7 1.2 The quantum scale . 8 1.2.1 Planck's constant . 8 1.2.2 Diffusion . 9 1.2.3 Speed . 9 1.2.4 Length . 10 1.2.5 Energy . 10 1.2.6 Compressibility . 10 1.2.7 Frequency . 11 1.2.8 Other length scales . 11 1.3 The Schr¨odingerequation . 11 1.3.1 The equation . 11 1.3.2 Density and current . 13 1.3.3 Osmotic and current velocity . 14 1.3.4 Momentum . 15 1.3.5 Energy . 15 1.4 The uncertainty principle . 17 1.5 Quantum stability . 18 2 Hilbert Space 21 2.1 Definition . 21 2.2 Self-duality . 24 2.3 Projections . 26 2.4 Operators . 27 2.5 Bounded operators . 28 1 2 CONTENTS 3 Function Spaces 31 3.1 Integration . 31 3.2 Function spaces . 34 3.3 Convergence . 36 3.4 Integral operators . 37 4 Bases 41 4.1 Discrete bases . 41 4.2 Continuous bases . 42 5 Spectral Representations 49 5.1 Multiplication operators . 49 5.2 Spectral representations . 50 5.3 Translation . 52 5.4 Approximate delta functions . 53 5.5 The Fourier transform . 54 5.6 Free motion . 57 5.6.1 Diffusion . 57 5.6.2 Oscillation . 58 6 The Harmonic Oscillator 61 6.1 The classical harmonic oscillator . 61 6.2 The one dimensional quantum harmonic oscillator . 62 6.3 The Fourier transform . 65 6.4 Density theorems . 65 6.5 The isotropic quantum harmonic oscillator . -
Notes on Von Neumann Algebras
Notes on von Neumann algebras Jesse Peterson April 5, 2013 2 Chapter 1 Spectral theory If A is a complex unital algebra then we denote by G(A) the set of elements which have a two sided inverse. If x 2 A, the spectrum of x is σA(x) = fλ 2 C j x − λ 62 G(A)g: The complement of the spectrum is called the resolvent and denoted ρA(x). Proposition 1.0.1. Let A be a unital algebra over C, and consider x; y 2 A. Then σA(xy) [ f0g = σA(yx) [ f0g. Proof. If 1 − xy 2 G(A) then we have (1 − yx)(1 + y(1 − xy)−1x) = 1 − yx + y(1 − xy)−1x − yxy(1 − xy)−1x = 1 − yx + y(1 − xy)(1 − xy)−1x = 1: Similarly, we have (1 + y(1 − xy)−1x)(1 − yx) = 1; and hence 1 − yx 2 G(A). Knowing the formula for the inverse beforehand of course made the proof of the previous proposition quite a bit easier. But this formula is quite natural to consider. Indeed, if we just consider formal power series then we have 1 1 X X (1 − yx)−1 = (yx)k = 1 + y( (xy)k)x = 1 + y(1 − xy)−1x: k=0 k=0 1.1 Banach and C∗-algebras A Banach algebra is a Banach space A, which is also an algebra such that kxyk ≤ kxkkyk: 3 4 CHAPTER 1. SPECTRAL THEORY A Banach algebra A is involutive if it possesses an anti-linear involution ∗, such that kx∗k = kxk, for all x 2 A. -
Notes on Von Neumann Algebras
Von Neumann Algebras. Vaughan F.R. Jones 1 October 1, 2009 1Supported in part by NSF Grant DMS93–22675, the Marsden fund UOA520, and the Swiss National Science Foundation. 2 Chapter 1 Introduction. The purpose of these notes is to provide a rapid introduction to von Neumann algebras which gets to the examples and active topics with a minimum of technical baggage. In this sense it is opposite in spirit from the treatises of Dixmier [], Takesaki[], Pedersen[], Kadison-Ringrose[], Stratila-Zsido[]. The philosophy is to lavish attention on a few key results and examples, and we prefer to make simplifying assumptions rather than go for the most general case. Thus we do not hesitate to give several proofs of a single result, or repeat an argument with different hypotheses. The notes are built around semester- long courses given at UC Berkeley though they contain more material than could be taught in a single semester. The notes are informal and the exercises are an integral part of the ex- position. These exercises are vital and mostly intended to be easy. 3 4 Chapter 2 Background and Prerequisites 2.1 Hilbert Space A Hilbert Space is a complex vector space H with inner product h; i : HxH! C which is linear in the first variable, satisfies hξ; ηi = hη; ξi, is positive definite, i.e. hξ; ξi > 0 for ξ 6= 0, and is complete for the norm defined by test jjξjj = phξ; ξi. Exercise 2.1.1. Prove the parallelogram identity : jjξ − ηjj2 + jjξ + ηjj2 = 2(jjξjj2 + jjηjj2) and the Cauchy-Schwartz inequality: jhξ; ηij ≤ jjξjj jjηjj: Theorem 2.1.2. -
Spectrum (Functional Analysis) - Wikipedia, the Free Encyclopedia
Spectrum (functional analysis) - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Spectrum_(functional_analysis) Spectrum (functional analysis) From Wikipedia, the free encyclopedia In functional analysis, the concept of the spectrum of a bounded operator is a generalisation of the concept of eigenvalues for matrices. Specifically, a complex number λ is said to be in the spectrum of a bounded linear operator T if λI − T is not invertible, where I is the identity operator. The study of spectra and related properties is known as spectral theory, which has numerous applications, most notably the mathematical formulation of quantum mechanics. The spectrum of an operator on a finite-dimensional vector space is precisely the set of eigenvalues. However an operator on an infinite-dimensional space may have additional elements in its spectrum, and may have no eigenvalues. For example, consider the right shift operator R on the Hilbert space ℓ2, This has no eigenvalues, since if Rx=λx then by expanding this expression we see that x1=0, x2=0, etc. On the other hand 0 is in the spectrum because the operator R − 0 (i.e. R itself) is not invertible: it is not surjective since any vector with non-zero first component is not in its range. In fact every bounded linear operator on a complex Banach space must have a non-empty spectrum. The notion of spectrum extends to densely-defined unbounded operators. In this case a complex number λ is said to be in the spectrum of such an operator T:D→X (where D is dense in X) if there is no bounded inverse (λI − T)−1:X→D. -
Finite Von Neumann Algebras : Examples and Some Basics 2 1.1
ERGODIC THEORY AND VON NEUMANN ALGEBRAS : AN INTRODUCTION CLAIRE ANANTHARAMAN-DELAROCHE Contents 1. Finite von Neumann algebras : examples and some basics 2 1.1. Notation and preliminaries 2 1.2. Measure space von Neumann algebras 5 1.3. Group von Neumann algebras 5 1.4. Standard form 9 1.5. Group measure space von Neumann algebras 11 1.6. Von Neumann algebras from equivalence relations 16 1.7. Two non-isomorphic II1 factors 20 Exercises 23 2. About factors arising from equivalence relations 25 2.1. Isomorphisms of equivalence relations vs isomorphisms of their von Neumann algebras 25 2.2. Cartan subalgebras 25 2.3. An application: computation of fundamental groups 26 Exercise 27 1 2 1 2 3. Study of the inclusion L (T ) ⊂ L (T ) o F2 29 3.1. The Haagerup property 30 3.2. Relative property (T) 31 References 35 1 2 CLAIRE ANANTHARAMAN-DELAROCHE 1. Finite von Neumann algebras : examples and some basics This section presents the basic constructions of von Neumann algebras coming from measure theory, group theory, group actions and equivalence relations. All this examples are naturally equipped with a faithful trace and are naturally represented on a Hilbert space. This provides a plentiful source of tracial von Neumann algebras to play with. 1.1. Notation and preliminaries. Let H be a complex Hilbert space1 with inner-product h·; ·i (always assumed to be antilinear in the first vari- able), and let B(H) be the algebra of all bounded linear operators from H to H. Equipped with the involution x 7! x∗ (adjoint of x) and with the operator norm, B(H) is a Banach ∗-algebra with unit IdH.