Spectral Theory and Applications. an Elementary Introductory Course
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The Transfer Operator Approach to ``Quantum Chaos
Front Modular surface Spectral theory Geodesics and coding Transfer operator Connection to spectral theory Conclusions Front page The transfer operator approach to “quantum chaos” Classical mechanics and the Laplace-Beltrami operator on PSL (Z) H 2 \ Tobias M¨uhlenbruch Joint work with D. Mayer and F. Str¨omberg Institut f¨ur Mathematik TU Clausthal [email protected] 22 January 2009, Fernuniversit¨at in Hagen Front Modular surface Spectral theory Geodesics and coding Transfer operator Connection to spectral theory Conclusions Outline of the presentation 1 Modular surface 2 Spectral theory 3 Geodesics and coding 4 Transfer operator 5 Connection to spectral theory 6 Conclusions Front Modular surface Spectral theory Geodesics and coding Transfer operator Connection to spectral theory Conclusions PSL Z The full modular group 2( ) Let PSL2(Z) be the full modular group PSL (Z)= S, T (ST )3 =1 = SL (Z) mod 1 2 h | i 2 {± } 0 1 1 1 1 0 with S = − , T = and 1 = . 1 0 0 1 0 1 a b az+b if z C, M¨obius transformations: z = cz+d ∈ c d a if z = . c ∞ Three orbits: the upper halfplane H = x + iy; y > 0 , the 1 { } projective real line PR and the lower half plane. z , z are PSL (Z)-equivalent if M PSL (Z) with 1 2 2 ∃ ∈ 2 M z1 = z2. The full modular group PSL2(Z) is generated by 1 translation T : z z + 1 and inversion S : z − . 7→ 7→ z (Closed) fundamental domain = z H; z 1, Re(z) 1 . F ∈ | |≥ | |≤ 2 Front Modular surface Spectral theory Geodesics and coding Transfer operator Connection to spectral theory Conclusions PSL Z H The modular surface 2( )\ The upper half plane H = x + iy; y > 0 can be viewed as a hyperbolic plane with constant{ negative} curvature 1. -
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. -
Criss-Cross Type Algorithms for Computing the Real Pseudospectral Abscissa
CRISS-CROSS TYPE ALGORITHMS FOR COMPUTING THE REAL PSEUDOSPECTRAL ABSCISSA DING LU∗ AND BART VANDEREYCKEN∗ Abstract. The real "-pseudospectrum of a real matrix A consists of the eigenvalues of all real matrices that are "-close to A. The closeness is commonly measured in spectral or Frobenius norm. The real "-pseudospectral abscissa, which is the largest real part of these eigenvalues for a prescribed value ", measures the structured robust stability of A. In this paper, we introduce a criss-cross type algorithm to compute the real "-pseudospectral abscissa for the spectral norm. Our algorithm is based on a superset characterization of the real pseudospectrum where each criss and cross search involves solving linear eigenvalue problems and singular value optimization problems. The new algorithm is proved to be globally convergent, and observed to be locally linearly convergent. Moreover, we propose a subspace projection framework in which we combine the criss-cross algorithm with subspace projection techniques to solve large-scale problems. The subspace acceleration is proved to be locally superlinearly convergent. The robustness and efficiency of the proposed algorithms are demonstrated on numerical examples. Key words. eigenvalue problem, real pseudospectra, spectral abscissa, subspace methods, criss- cross methods, robust stability AMS subject classifications. 15A18, 93B35, 30E10, 65F15 1. Introduction. The real "-pseudospectrum of a matrix A 2 Rn×n is defined as R n×n (1) Λ" (A) = fλ 2 C : λ 2 Λ(A + E) with E 2 R ; kEk ≤ "g; where Λ(A) denotes the eigenvalues of a matrix A and k · k is the spectral norm. It is also known as the real unstructured spectral value set (see, e.g., [9, 13, 11]) and it can be regarded as a generalization of the more standard complex pseudospectrum (see, e.g., [23, 24]). -
Arxiv:1910.08491V3 [Math.ST] 3 Nov 2020
Weakly stationary stochastic processes valued in a separable Hilbert space: Gramian-Cram´er representations and applications Amaury Durand ∗† Fran¸cois Roueff ∗ September 14, 2021 Abstract The spectral theory for weakly stationary processes valued in a separable Hilbert space has known renewed interest in the past decade. However, the recent literature on this topic is often based on restrictive assumptions or lacks important insights. In this paper, we follow earlier approaches which fully exploit the normal Hilbert module property of the space of Hilbert- valued random variables. This approach clarifies and completes the isomorphic relationship between the modular spectral domain to the modular time domain provided by the Gramian- Cram´er representation. We also discuss the general Bochner theorem and provide useful results on the composition and inversion of lag-invariant linear filters. Finally, we derive the Cram´er-Karhunen-Lo`eve decomposition and harmonic functional principal component analysis without relying on simplifying assumptions. 1 Introduction Functional data analysis has become an active field of research in the recent decades due to technological advances which makes it possible to store longitudinal data at very high frequency (see e.g. [22, 31]), or complex data e.g. in medical imaging [18, Chapter 9], [15], linguistics [28] or biophysics [27]. In these frameworks, the data is seen as valued in an infinite dimensional separable Hilbert space thus isomorphic to, and often taken to be, the function space L2(0, 1) of Lebesgue-square-integrable functions on [0, 1]. In this setting, a 2 functional time series refers to a bi-sequences (Xt)t∈Z of L (0, 1)-valued random variables and the assumption of finite second moment means that each random variable Xt belongs to the L2 Bochner space L2(Ω, F, L2(0, 1), P) of measurable mappings V : Ω → L2(0, 1) such that E 2 kV kL2(0,1) < ∞ , where k·kL2(0,1) here denotes the norm endowing the Hilbert space L2h(0, 1). -
An Introduction to Fractal Uncertainty Principle
An introduction to fractal uncertainty principle Semyon Dyatlov (MIT / UC Berkeley) The goal of this minicourse is to give a brief introduction to fractal uncertainty principle and its applications to transfer operators for Schottky groups Part 1: Schottky groups, transfer operators, and resonances Schottky groups of SL ( 2 IR ) on the action , Using ' 70 d z 3- H = fz E l Im its and on boundary H = IRV { a } by Mobius transformations : f- (Ibd ) ⇒ 8. z = aztbcztd Schottky groups provide interesting nonlinear dynamics on fractal limit sets and appear in many important applications To define a Schottky group, we fix: of r disks • 2. a collection nonintersecting IR in ¢ with centers in := . AIR Q . , Dzr Ij Dj := and 1 . • denote A { ,2r3 . Ifa Er Atr if I = , f - r if rsaE2r a , that • 8 . such , . Kr fix maps . , ' )= ti racial D; Da , E- Pc SLC 2,112) • The Schottky group free ti . is the group generated by . .fr Example of a Schottky group Here is a picture for the case of 4 disks: IC = = Cil K ( ) Dz , 8. ( Dj ) Dz , IDE - =D ID ) - Ds ( IC ID:) BCCI : , Oy , 83 ⇒ A Schottky quotients ' of Ta k i n g the quotient CH , ) action of P we a the , get by surface co - . convex compact hyperbolic µ ' - M - T l H IIF plane # Three-funneled surface in G - funnel infinite end Words and nested intervals the = 2r encodes Recall I 91 . } that , . P of the I := at r generators group , n : • Words of length " = W . an far / Vj , ajt, taj } ' = : . ⇒ = . ⑨ . - A , An OT A , Ah i • Group elements : : = E T - . -
Reproducing Kernel Hilbert Space Compactification of Unitary Evolution
Reproducing kernel Hilbert space compactification of unitary evolution groups Suddhasattwa Dasa, Dimitrios Giannakisa,∗, Joanna Slawinskab aCourant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA bFinnish Center for Artificial Intelligence, Department of Computer Science, University of Helsinki, Helsinki, Finland Abstract A framework for coherent pattern extraction and prediction of observables of measure-preserving, ergodic dynamical systems with both atomic and continuous spectral components is developed. This framework is based on an approximation of the generator of the system by a compact operator Wτ on a reproducing kernel Hilbert space (RKHS). A key element of this approach is that Wτ is skew-adjoint (unlike regularization approaches based on the addition of diffusion), and thus can be characterized by a unique projection- valued measure, discrete by compactness, and an associated orthonormal basis of eigenfunctions. These eigenfunctions can be ordered in terms of a Dirichlet energy on the RKHS, and provide a notion of coherent observables under the dynamics akin to the Koopman eigenfunctions associated with the atomic part of the spectrum. In addition, the regularized generator has a well-defined Borel functional calculus allowing tWτ the construction of a unitary evolution group fe gt2R on the RKHS, which approximates the unitary Koopman evolution group of the original system. We establish convergence results for the spectrum and Borel functional calculus of the regularized generator to those of the original system in the limit τ ! 0+. Convergence results are also established for a data-driven formulation, where these operators are approximated using finite-rank operators obtained from observed time series. An advantage of working in spaces of observables with an RKHS structure is that one can perform pointwise evaluation and interpolation through bounded linear operators, which is not possible in Lp spaces. -
Asymptotic Spectral Gap and Weyl Law for Ruelle Resonances of Open Partially Expanding Maps Jean-François Arnoldi, Frédéric Faure, Tobias Weich
Asymptotic spectral gap and Weyl law for Ruelle resonances of open partially expanding maps Jean-François Arnoldi, Frédéric Faure, Tobias Weich To cite this version: Jean-François Arnoldi, Frédéric Faure, Tobias Weich. Asymptotic spectral gap and Weyl law for Ruelle resonances of open partially expanding maps. Ergodic Theory and Dynamical Systems, Cambridge University Press (CUP), 2017, 37 (1), pp.1-58. 10.1017/etds.2015.34. hal-00787781v2 HAL Id: hal-00787781 https://hal.archives-ouvertes.fr/hal-00787781v2 Submitted on 4 Jun 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Asymptotic spectral gap and Weyl law for Ruelle resonances of open partially expanding maps Jean Francois Arnoldi∗, Frédéric Faure†, Tobias Weich‡ 01-06-2013 Abstract We consider a simple model of an open partially expanding map. Its trapped set in phase space is a fractal set. We first show that there is a well defined K discrete spectrum of Ruelle resonances which describes the asymptotisc of correlation functions for large time and which is parametrized by the Fourier component ν on the neutral direction of the dynamics. We introduce a specific hypothesis on the dynamics that we call “minimal captivity”. -
Notes on the Spectral Theorem
Math 108b: Notes on the Spectral Theorem From section 6.3, we know that every linear operator T on a finite dimensional inner prod- uct space V has an adjoint. (T ∗ is defined as the unique linear operator on V such that hT (x), yi = hx, T ∗(y)i for every x, y ∈ V – see Theroems 6.8 and 6.9.) When V is infinite dimensional, the adjoint T ∗ may or may not exist. One useful fact (Theorem 6.10) is that if β is an orthonormal basis for a finite dimen- ∗ ∗ sional inner product space V , then [T ]β = [T ]β. That is, the matrix representation of the operator T ∗ is equal to the complex conjugate of the matrix representation for T . For a general vector space V , and a linear operator T , we have already asked the ques- tion “when is there a basis of V consisting only of eigenvectors of T ?” – this is exactly when T is diagonalizable. Now, for an inner product space V , we know how to check whether vec- tors are orthogonal, and we know how to define the norms of vectors, so we can ask “when is there an orthonormal basis of V consisting only of eigenvectors of T ?” Clearly, if there is such a basis, T is diagonalizable – and moreover, eigenvectors with distinct eigenvalues must be orthogonal. Definitions Let V be an inner product space. Let T ∈ L(V ). (a) T is normal if T ∗T = TT ∗ (b) T is self-adjoint if T ∗ = T For the next two definitions, assume V is finite-dimensional: Then, (c) T is unitary if F = C and kT (x)k = kxk for every x ∈ V (d) T is orthogonal if F = R and kT (x)k = kxk for every x ∈ V Notes 1. -
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. -
The Spectral Theorem for Self-Adjoint and Unitary Operators Michael Taylor Contents 1. Introduction 2. Functions of a Self-Adjoi
The Spectral Theorem for Self-Adjoint and Unitary Operators Michael Taylor Contents 1. Introduction 2. Functions of a self-adjoint operator 3. Spectral theorem for bounded self-adjoint operators 4. Functions of unitary operators 5. Spectral theorem for unitary operators 6. Alternative approach 7. From Theorem 1.2 to Theorem 1.1 A. Spectral projections B. Unbounded self-adjoint operators C. Von Neumann's mean ergodic theorem 1 2 1. Introduction If H is a Hilbert space, a bounded linear operator A : H ! H (A 2 L(H)) has an adjoint A∗ : H ! H defined by (1.1) (Au; v) = (u; A∗v); u; v 2 H: We say A is self-adjoint if A = A∗. We say U 2 L(H) is unitary if U ∗ = U −1. More generally, if H is another Hilbert space, we say Φ 2 L(H; H) is unitary provided Φ is one-to-one and onto, and (Φu; Φv)H = (u; v)H , for all u; v 2 H. If dim H = n < 1, each self-adjoint A 2 L(H) has the property that H has an orthonormal basis of eigenvectors of A. The same holds for each unitary U 2 L(H). Proofs can be found in xx11{12, Chapter 2, of [T3]. Here, we aim to prove the following infinite dimensional variant of such a result, called the Spectral Theorem. Theorem 1.1. If A 2 L(H) is self-adjoint, there exists a measure space (X; F; µ), a unitary map Φ: H ! L2(X; µ), and a 2 L1(X; µ), such that (1.2) ΦAΦ−1f(x) = a(x)f(x); 8 f 2 L2(X; µ): Here, a is real valued, and kakL1 = kAk. -
7 Spectral Properties of Matrices
7 Spectral Properties of Matrices 7.1 Introduction The existence of directions that are preserved by linear transformations (which are referred to as eigenvectors) has been discovered by L. Euler in his study of movements of rigid bodies. This work was continued by Lagrange, Cauchy, Fourier, and Hermite. The study of eigenvectors and eigenvalues acquired in- creasing significance through its applications in heat propagation and stability theory. Later, Hilbert initiated the study of eigenvalue in functional analysis (in the theory of integral operators). He introduced the terms of eigenvalue and eigenvector. The term eigenvalue is a German-English hybrid formed from the German word eigen which means “own” and the English word “value”. It is interesting that Cauchy referred to the same concept as characteristic value and the term characteristic polynomial of a matrix (which we introduce in Definition 7.1) was derived from this naming. We present the notions of geometric and algebraic multiplicities of eigen- values, examine properties of spectra of special matrices, discuss variational characterizations of spectra and the relationships between matrix norms and eigenvalues. We conclude this chapter with a section dedicated to singular values of matrices. 7.2 Eigenvalues and Eigenvectors Let A Cn×n be a square matrix. An eigenpair of A is a pair (λ, x) C (Cn∈ 0 ) such that Ax = λx. We refer to λ is an eigenvalue and to ∈x is× an eigenvector−{ } . The set of eigenvalues of A is the spectrum of A and will be denoted by spec(A). If (λ, x) is an eigenpair of A, the linear system Ax = λx has a non-trivial solution in x.