Lecture Notes Spectral Theory
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An Elementary Proof of the Spectral Radius Formula for Matrices
AN ELEMENTARY PROOF OF THE SPECTRAL RADIUS FORMULA FOR MATRICES JOEL A. TROPP Abstract. We present an elementary proof that the spectral ra- dius of a matrix A may be obtained using the formula 1 ρ(A) = lim kAnk =n; n!1 where k · k represents any matrix norm. 1. Introduction It is a well-known fact from the theory of Banach algebras that the spectral radius of any element A is given by the formula ρ(A) = lim kAnk1=n: (1.1) n!1 For a matrix, the spectrum is just the collection of eigenvalues, so this formula yields a technique for estimating for the top eigenvalue. The proof of Equation 1.1 is beautiful but advanced. See, for exam- ple, Rudin's treatment in his Functional Analysis. It turns out that elementary techniques suffice to develop the formula for matrices. 2. Preliminaries For completeness, we shall briefly introduce the major concepts re- quired in the proof. It is expected that the reader is already familiar with these ideas. 2.1. Norms. A norm is a mapping k · k from a vector space X into the nonnegative real numbers R+ which has three properties: (1) kxk = 0 if and only if x = 0; (2) kαxk = jαj kxk for any scalar α and vector x; and (3) kx + yk ≤ kxk + kyk for any vectors x and y. The most fundamental example of a norm is the Euclidean norm k·k2 which corresponds to the standard topology on Rn. It is defined by 2 2 2 kxk2 = k(x1; x2; : : : ; xn)k2 = x1 + x2 + · · · + xn: Date: 30 November 2001. -
1 Bounded and Unbounded Operators
1 1 Bounded and unbounded operators 1. Let X, Y be Banach spaces and D 2 X a linear space, not necessarily closed. 2. A linear operator is any linear map T : D ! Y . 3. D is the domain of T , sometimes written Dom (T ), or D (T ). 4. T (D) is the Range of T , Ran(T ). 5. The graph of T is Γ(T ) = f(x; T x)jx 2 D (T )g 6. The kernel of T is Ker(T ) = fx 2 D (T ): T x = 0g 1.1 Operations 1. aT1 + bT2 is defined on D (T1) \D (T2). 2. if T1 : D (T1) ⊂ X ! Y and T2 : D (T2) ⊂ Y ! Z then T2T1 : fx 2 D (T1): T1(x) 2 D (T2). In particular, inductively, D (T n) = fx 2 D (T n−1): T (x) 2 D (T )g. The domain may become trivial. 3. Inverse. The inverse is defined if Ker(T ) = f0g. This implies T is bijective. Then T −1 : Ran(T ) !D (T ) is defined as the usual function inverse, and is clearly linear. @ is not invertible on C1[0; 1]: Ker@ = C. 4. Closable operators. It is natural to extend functions by continuity, when possible. If xn ! x and T xn ! y we want to see whether we can define T x = y. Clearly, we must have xn ! 0 and T xn ! y ) y = 0; (1) since T (0) = 0 = y. Conversely, (1) implies the extension T x := y when- ever xn ! x and T xn ! y is consistent and defines a linear operator. -
The Index of Normal Fredholm Elements of C* -Algebras
proceedings of the american mathematical society Volume 113, Number 1, September 1991 THE INDEX OF NORMAL FREDHOLM ELEMENTS OF C*-ALGEBRAS J. A. MINGO AND J. S. SPIELBERG (Communicated by Palle E. T. Jorgensen) Abstract. Examples are given of normal elements of C*-algebras that are invertible modulo an ideal and have nonzero index, in contrast to the case of Fredholm operators on Hubert space. It is shown that this phenomenon occurs only along the lines of these examples. Let T be a bounded operator on a Hubert space. If the range of T is closed and both T and T* have a finite dimensional kernel then T is Fredholm, and the index of T is dim(kerT) - dim(kerT*). If T is normal then kerT = ker T*, so a normal Fredholm operator has index 0. Let us consider a generalization of the notion of Fredholm operator intro- duced by Atiyah. Let X be a compact Hausdorff space and consider continuous functions T: X —>B(H), where B(H) is the set of bounded linear operators on a separable infinite dimensional Hubert space with the norm topology. The set of such functions forms a C*- algebra C(X) <g>B(H). A function T is Fredholm if T(x) is Fredholm for each x . Atiyah [1, Appendix] showed how such an element has an index which is an element of K°(X). Suppose that T is Fredholm and T(x) is normal for each x. Is the index of T necessarily 0? There is a generalization of this question that we would like to consider. -
Volterra-Choquet Nonlinear Operators
Volterra-Choquet nonlinear operators Sorin G. Gal Department of Mathematics and Computer Science, University of Oradea, Universitatii Street No.1, 410087, Oradea, Romania E-mail: [email protected] and Academy of Romanian Scientists, Splaiul Independentei nr. 54 050094 Bucharest, Romania Abstract In this paper we study to what extend some properties of the classical linear Volterra operators could be transferred to the nonlinear Volterra- Choquet operators, obtained by replacing the classical linear integral with respect to the Lebesgue measure, by the nonlinear Choquet integral with respect to a nonadditive set function. Compactness, Lipschitz and cyclic- ity properties are studied. MSC(2010): 47H30, 28A12, 28A25. Keywords and phrases: Choquet integral, monotone, submodular and continuous from below set function, Choquet Lp-space, distorted Lebesgue mea- sures, Volterra-Choquet nonlinear operator, compactness, Lipschitz properties, cyclicity. 1 Introduction Inspired by the electrostatic capacity, G. Choquet has introduced in [5] (see also arXiv:2003.00004v1 [math.CA] 28 Feb 2020 [6]) a concept of integral with respect to a non-additive set function which, in the case when the underlying set function is a σ-additive measure, coincides with the Lebesgue integral. Choquet integral is proved to be a powerful and useful tool in decision making under risk and uncertainty, finance, economics, insurance, pattern recognition, etc (see, e.g., [37] and [38] as well as the references therein). Many new interesting results were obtained as analogs in the framework of Choquet integral of certain known results for the Lebesgue integral. In this sense, we can mention here, for example, the contributions to function spaces theory in [4], to potential theory in [1], to approximation theory in [13]-[17] and to integral equations theory in [18], [19]. -
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. -
18.102 Introduction to Functional Analysis Spring 2009
MIT OpenCourseWare http://ocw.mit.edu 18.102 Introduction to Functional Analysis Spring 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 108 LECTURE NOTES FOR 18.102, SPRING 2009 Lecture 19. Thursday, April 16 I am heading towards the spectral theory of self-adjoint compact operators. This is rather similar to the spectral theory of self-adjoint matrices and has many useful applications. There is a very effective spectral theory of general bounded but self- adjoint operators but I do not expect to have time to do this. There is also a pretty satisfactory spectral theory of non-selfadjoint compact operators, which it is more likely I will get to. There is no satisfactory spectral theory for general non-compact and non-self-adjoint operators as you can easily see from examples (such as the shift operator). In some sense compact operators are ‘small’ and rather like finite rank operators. If you accept this, then you will want to say that an operator such as (19.1) Id −K; K 2 K(H) is ‘big’. We are quite interested in this operator because of spectral theory. To say that λ 2 C is an eigenvalue of K is to say that there is a non-trivial solution of (19.2) Ku − λu = 0 where non-trivial means other than than the solution u = 0 which always exists. If λ =6 0 we can divide by λ and we are looking for solutions of −1 (19.3) (Id −λ K)u = 0 −1 which is just (19.1) for another compact operator, namely λ K: What are properties of Id −K which migh show it to be ‘big? Here are three: Proposition 26. -
Curl, Divergence and Laplacian
Curl, Divergence and Laplacian What to know: 1. The definition of curl and it two properties, that is, theorem 1, and be able to predict qualitatively how the curl of a vector field behaves from a picture. 2. The definition of divergence and it two properties, that is, if div F~ 6= 0 then F~ can't be written as the curl of another field, and be able to tell a vector field of clearly nonzero,positive or negative divergence from the picture. 3. Know the definition of the Laplace operator 4. Know what kind of objects those operator take as input and what they give as output. The curl operator Let's look at two plots of vector fields: Figure 1: The vector field Figure 2: The vector field h−y; x; 0i: h1; 1; 0i We can observe that the second one looks like it is rotating around the z axis. We'd like to be able to predict this kind of behavior without having to look at a picture. We also promised to find a criterion that checks whether a vector field is conservative in R3. Both of those goals are accomplished using a tool called the curl operator, even though neither of those two properties is exactly obvious from the definition we'll give. Definition 1. Let F~ = hP; Q; Ri be a vector field in R3, where P , Q and R are continuously differentiable. We define the curl operator: @R @Q @P @R @Q @P curl F~ = − ~i + − ~j + − ~k: (1) @y @z @z @x @x @y Remarks: 1. -
Arxiv:0906.4883V2 [Math.CA] 4 Jan 2010 Ro O Olna Yeblcsses[4.W Hwblwta El’ T Helly’S That Theorem
THE KOLMOGOROV–RIESZ COMPACTNESS THEOREM HARALD HANCHE-OLSEN AND HELGE HOLDEN Abstract. We show that the Arzelà–Ascoli theorem and Kolmogorov com- pactness theorem both are consequences of a simple lemma on compactness in metric spaces. Their relation to Helly’s theorem is discussed. The paper contains a detailed discussion on the historical background of the Kolmogorov compactness theorem. 1. Introduction Compactness results in the spaces Lp(Rd) (1 p < ) are often vital in exis- tence proofs for nonlinear partial differential equations.≤ ∞ A necessary and sufficient condition for a subset of Lp(Rd) to be compact is given in what is often called the Kolmogorov compactness theorem, or Fréchet–Kolmogorov compactness theorem. Proofs of this theorem are frequently based on the Arzelà–Ascoli theorem. We here show how one can deduce both the Kolmogorov compactness theorem and the Arzelà–Ascoli theorem from one common lemma on compactness in metric spaces, which again is based on the fact that a metric space is compact if and only if it is complete and totally bounded. Furthermore, we trace out the historical roots of Kolmogorov’s compactness theorem, which originated in Kolmogorov’s classical paper [18] from 1931. However, there were several other approaches to the issue of describing compact subsets of Lp(Rd) prior to and after Kolmogorov, and several of these are described in Section 4. Furthermore, extensions to other spaces, say Lp(Rd) (0 p < 1), Orlicz spaces, or compact groups, are described. Helly’s theorem is often≤ used as a replacement for Kolmogorov’s compactness theorem, in particular in the context of nonlinear hyperbolic conservation laws, in spite of being more specialized (e.g., in the sense that its classical version requires one spatial dimension). -
On the Origin and Early History of Functional Analysis
U.U.D.M. Project Report 2008:1 On the origin and early history of functional analysis Jens Lindström Examensarbete i matematik, 30 hp Handledare och examinator: Sten Kaijser Januari 2008 Department of Mathematics Uppsala University Abstract In this report we will study the origins and history of functional analysis up until 1918. We begin by studying ordinary and partial differential equations in the 18th and 19th century to see why there was a need to develop the concepts of functions and limits. We will see how a general theory of infinite systems of equations and determinants by Helge von Koch were used in Ivar Fredholm’s 1900 paper on the integral equation b Z ϕ(s) = f(s) + λ K(s, t)f(t)dt (1) a which resulted in a vast study of integral equations. One of the most enthusiastic followers of Fredholm and integral equation theory was David Hilbert, and we will see how he further developed the theory of integral equations and spectral theory. The concept introduced by Fredholm to study sets of transformations, or operators, made Maurice Fr´echet realize that the focus should be shifted from particular objects to sets of objects and the algebraic properties of these sets. This led him to introduce abstract spaces and we will see how he introduced the axioms that defines them. Finally, we will investigate how the Lebesgue theory of integration were used by Frigyes Riesz who was able to connect all theory of Fredholm, Fr´echet and Lebesgue to form a general theory, and a new discipline of mathematics, now known as functional analysis. -
Stat 309: Mathematical Computations I Fall 2018 Lecture 4
STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 4 1. spectral radius • matrix 2-norm is also known as the spectral norm • name is connected to the fact that the norm is given by the square root of the largest eigenvalue of ATA, i.e., largest singular value of A (more on this later) n×n • in general, the spectral radius ρ(A) of a matrix A 2 C is defined in terms of its largest eigenvalue ρ(A) = maxfjλij : Axi = λixi; xi 6= 0g n×n • note that the spectral radius does not define a norm on C • for example the non-zero matrix 0 1 J = 0 0 has ρ(J) = 0 since both its eigenvalues are 0 • there are some relationships between the norm of a matrix and its spectral radius • the easiest one is that ρ(A) ≤ kAk n for any matrix norm that satisfies the inequality kAxk ≤ kAkkxk for all x 2 C , i.e., consistent norm { here's a proof: Axi = λixi taking norms, kAxik = kλixik = jλijkxik therefore kAxik jλij = ≤ kAk kxik since this holds for any eigenvalue of A, it follows that maxjλij = ρ(A) ≤ kAk i { in particular this is true for any operator norm { this is in general not true for norms that do not satisfy the consistency inequality kAxk ≤ kAkkxk (thanks to Likai Chen for pointing out); for example the matrix " p1 p1 # A = 2 2 p1 − p1 2 2 p is orthogonal and therefore ρ(A) = 1 but kAkH;1 = 1= 2 and so ρ(A) > kAkH;1 { exercise: show that any eigenvalue of a unitary or an orthogonal matrix must have absolute value 1 Date: October 10, 2018, version 1.0. -
The Notion of Observable and the Moment Problem for ∗-Algebras and Their GNS Representations
The notion of observable and the moment problem for ∗-algebras and their GNS representations Nicol`oDragoa, Valter Morettib Department of Mathematics, University of Trento, and INFN-TIFPA via Sommarive 14, I-38123 Povo (Trento), Italy. [email protected], [email protected] February, 17, 2020 Abstract We address some usually overlooked issues concerning the use of ∗-algebras in quantum theory and their physical interpretation. If A is a ∗-algebra describing a quantum system and ! : A ! C a state, we focus in particular on the interpretation of !(a) as expectation value for an algebraic observable a = a∗ 2 A, studying the problem of finding a probability n measure reproducing the moments f!(a )gn2N. This problem enjoys a close relation with the selfadjointness of the (in general only symmetric) operator π!(a) in the GNS representation of ! and thus it has important consequences for the interpretation of a as an observable. We n provide physical examples (also from QFT) where the moment problem for f!(a )gn2N does not admit a unique solution. To reduce this ambiguity, we consider the moment problem n ∗ (a) for the sequences f!b(a )gn2N, being b 2 A and !b(·) := !(b · b). Letting µ!b be a n solution of the moment problem for the sequence f!b(a )gn2N, we introduce a consistency (a) relation on the family fµ!b gb2A. We prove a 1-1 correspondence between consistent families (a) fµ!b gb2A and positive operator-valued measures (POVM) associated with the symmetric (a) operator π!(a). In particular there exists a unique consistent family of fµ!b gb2A if and only if π!(a) is maximally symmetric. -
Asymptotic Spectral Measures: Between Quantum Theory and E
Asymptotic Spectral Measures: Between Quantum Theory and E-theory Jody Trout Department of Mathematics Dartmouth College Hanover, NH 03755 Email: [email protected] Abstract— We review the relationship between positive of classical observables to the C∗-algebra of quantum operator-valued measures (POVMs) in quantum measurement observables. See the papers [12]–[14] and theB books [15], C∗ E theory and asymptotic morphisms in the -algebra -theory of [16] for more on the connections between operator algebra Connes and Higson. The theory of asymptotic spectral measures, as introduced by Martinez and Trout [1], is integrally related K-theory, E-theory, and quantization. to positive asymptotic morphisms on locally compact spaces In [1], Martinez and Trout showed that there is a fundamen- via an asymptotic Riesz Representation Theorem. Examples tal quantum-E-theory relationship by introducing the concept and applications to quantum physics, including quantum noise of an asymptotic spectral measure (ASM or asymptotic PVM) models, semiclassical limits, pure spin one-half systems and quantum information processing will also be discussed. A~ ~ :Σ ( ) { } ∈(0,1] →B H I. INTRODUCTION associated to a measurable space (X, Σ). (See Definition 4.1.) In the von Neumann Hilbert space model [2] of quantum Roughly, this is a continuous family of POV-measures which mechanics, quantum observables are modeled as self-adjoint are “asymptotically” projective (or quasiprojective) as ~ 0: → operators on the Hilbert space of states of the quantum system. ′ ′ A~(∆ ∆ ) A~(∆)A~(∆ ) 0 as ~ 0 The Spectral Theorem relates this theoretical view of a quan- ∩ − → → tum observable to the more operational one of a projection- for certain measurable sets ∆, ∆′ Σ.