Lecture Notes on the Spectral Theorem
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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. -
Appendix A. Measure and Integration
Appendix A. Measure and integration We suppose the reader is familiar with the basic facts concerning set theory and integration as they are presented in the introductory course of analysis. In this appendix, we review them briefly, and add some more which we shall need in the text. Basic references for proofs and a detailed exposition are, e.g., [[ H a l 1 ]] , [[ J a r 1 , 2 ]] , [[ K F 1 , 2 ]] , [[ L i L ]] , [[ R u 1 ]] , or any other textbook on analysis you might prefer. A.1 Sets, mappings, relations A set is a collection of objects called elements. The symbol card X denotes the cardi- nality of the set X. The subset M consisting of the elements of X which satisfy the conditions P1(x),...,Pn(x) is usually written as M = { x ∈ X : P1(x),...,Pn(x) }.A set whose elements are certain sets is called a system or family of these sets; the family of all subsystems of a given X is denoted as 2X . The operations of union, intersection, and set difference are introduced in the standard way; the first two of these are commutative, associative, and mutually distributive. In a { } system Mα of any cardinality, the de Morgan relations , X \ Mα = (X \ Mα)and X \ Mα = (X \ Mα), α α α α are valid. Another elementary property is the following: for any family {Mn} ,whichis { } at most countable, there is a disjoint family Nn of the same cardinality such that ⊂ \ ∪ \ Nn Mn and n Nn = n Mn.Theset(M N) (N M) is called the symmetric difference of the sets M,N and denoted as M #N. -
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. -
Weak and Norm Approximate Identities Are Different
Pacific Journal of Mathematics WEAK AND NORM APPROXIMATE IDENTITIES ARE DIFFERENT CHARLES ALLEN JONES AND CHARLES DWIGHT LAHR Vol. 72, No. 1 January 1977 PACIFIC JOURNAL OF MATHEMATICS Vol. 72, No. 1, 1977 WEAK AND NORM APPROXIMATE IDENTITIES ARE DIFFERENT CHARLES A. JONES AND CHARLES D. LAHR An example is given of a convolution measure algebra which has a bounded weak approximate identity, but no norm approximate identity. 1* Introduction* Let A be a commutative Banach algebra, Ar the dual space of A, and ΔA the maximal ideal space of A. A weak approximate identity for A is a net {e(X):\eΛ} in A such that χ(e(λ)α) > χ(α) for all αei, χ e A A. A norm approximate identity for A is a net {e(λ):λeΛ} in A such that ||β(λ)α-α|| >0 for all aeA. A net {e(X):\eΛ} in A is bounded and of norm M if there exists a positive number M such that ||e(λ)|| ^ M for all XeΛ. It is well known that if A has a bounded weak approximate identity for which /(e(λ)α) —>/(α) for all feA' and αeA, then A has a bounded norm approximate identity [1, Proposition 4, page 58]. However, the situation is different if weak convergence is with re- spect to ΔA and not A'. An example is given in § 2 of a Banach algebra A which has a weak approximate identity, but does not have a norm approximate identity. This algebra provides a coun- terexample to a theorem of J. -
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. -
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. -
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. -
Regularity Conditions for Banach Function Algebras
Regularity conditions for Banach function algebras Dr J. F. Feinstein University of Nottingham June 2009 1 1 Useful sources A very useful text for the material in this mini-course is the book Banach Algebras and Automatic Continuity by H. Garth Dales, London Mathematical Society Monographs, New Series, Volume 24, The Clarendon Press, Oxford, 2000. In particular, many of the examples and conditions discussed here may be found in Chapter 4 of that book. We shall refer to this book throughout as the book of Dales. Most of my e-prints are available from www.maths.nott.ac.uk/personal/jff/Papers Several of my research and teaching presentations are available from www.maths.nott.ac.uk/personal/jff/Beamer 2 2 Introduction to normed algebras and Banach algebras 2.1 Some problems to think about Those who have seen much of this introductory material before may wish to think about some of the following problems. We shall return to these problems at suitable points in this course. Problem 2.1.1 (Easy using standard theory!) It is standard that the set of all rational functions (quotients of polynomials) with complex coefficients is a field: this is a special case of the “field of fractions" of an integral domain. Question: Is there an algebra norm on this field (regarded as an algebra over C)? 3 Problem 2.1.2 (Very hard!) Does there exist a pair of sequences (λn), (an) of non-zero complex numbers such that (i) no two of the an are equal, P1 (ii) n=1 jλnj < 1, (iii) janj < 2 for all n 2 N, and yet, (iv) for all z 2 C, 1 X λn exp (anz) = 0? n=1 Gap to fill in 4 Problem 2.1.3 Denote by C[0; 1] the \trivial" uniform algebra of all continuous, complex-valued functions on [0; 1]. -
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. -
Proved for Real Hilbert Spaces. Time Derivatives of Observables and Applications
AN ABSTRACT OF THE THESIS OF BERNARD W. BANKSfor the degree DOCTOR OF PHILOSOPHY (Name) (Degree) in MATHEMATICS presented on (Major Department) (Date) Title: TIME DERIVATIVES OF OBSERVABLES AND APPLICATIONS Redacted for Privacy Abstract approved: Stuart Newberger LetA andH be self -adjoint operators on a Hilbert space. Conditions for the differentiability with respect totof -itH -itH <Ae cp e 9>are given, and under these conditionsit is shown that the derivative is<i[HA-AH]e-itHcp,e-itHyo>. These resultsare then used to prove Ehrenfest's theorem and to provide results on the behavior of the mean of position as a function of time. Finally, Stone's theorem on unitary groups is formulated and proved for real Hilbert spaces. Time Derivatives of Observables and Applications by Bernard W. Banks A THESIS submitted to Oregon State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy June 1975 APPROVED: Redacted for Privacy Associate Professor of Mathematics in charge of major Redacted for Privacy Chai an of Department of Mathematics Redacted for Privacy Dean of Graduate School Date thesis is presented March 4, 1975 Typed by Clover Redfern for Bernard W. Banks ACKNOWLEDGMENTS I would like to take this opportunity to thank those people who have, in one way or another, contributed to these pages. My special thanks go to Dr. Stuart Newberger who, as my advisor, provided me with an inexhaustible supply of wise counsel. I am most greatful for the manner he brought to our many conversa- tions making them into a mutual exchange between two enthusiasta I must also thank my parents for their support during the earlier years of my education.Their contributions to these pages are not easily descerned, but they are there never the less. -
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. -
On Relaxation of Normality in the Fuglede-Putnam Theorem
proceedings of the american mathematical society Volume 77, Number 3, December 1979 ON RELAXATION OF NORMALITY IN THE FUGLEDE-PUTNAM THEOREM TAKAYUKIFURUTA1 Abstract. An operator means a bounded linear operator on a complex Hubert space. The familiar Fuglede-Putnam theorem asserts that if A and B are normal operators and if X is an operator such that AX = XB, then A*X = XB*. We shall relax the normality in the hypotheses on A and B. Theorem 1. If A and B* are subnormal and if X is an operator such that AX = XB, then A*X = XB*. Theorem 2. Suppose A, B, X are operators in the Hubert space H such that AX = XB. Assume also that X is an operator of Hilbert-Schmidt class. Then A*X = XB* under any one of the following hypotheses: (i) A is k-quasihyponormal and B* is invertible hyponormal, (ii) A is quasihyponormal and B* is invertible hyponormal, (iii) A is nilpotent and B* is invertible hyponormal. 1. In this paper an operator means a bounded linear operator on a complex Hilbert space. An operator T is called quasinormal if F commutes with T* T, subnormal if T has a normal extension and hyponormal if [ F*, T] > 0 where [S, T] = ST - TS. The inclusion relation of the classes of nonnormal opera- tors listed above is as follows: Normal § Quasinormal § Subnormal ^ Hyponormal; the above inclusions are all proper [3, Problem 160, p. 101]. The familiar Fuglede-Putnam theorem is as follows ([3, Problem 152], [4]). Theorem A. If A and B are normal operators and if X is an operator such that AX = XB, then A*X = XB*.