Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
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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. -
Math 353 Lecture Notes Intro to Pdes Eigenfunction Expansions for Ibvps
Math 353 Lecture Notes Intro to PDEs Eigenfunction expansions for IBVPs J. Wong (Fall 2020) Topics covered • Eigenfunction expansions for PDEs ◦ The procedure for time-dependent problems ◦ Projection, independent evolution of modes 1 The eigenfunction method to solve PDEs We are now ready to demonstrate how to use the components derived thus far to solve the heat equation. First, two examples to illustrate the proces... 1.1 Example 1: no source; Dirichlet BCs The simplest case. We solve ut =uxx; x 2 (0; 1); t > 0 (1.1a) u(0; t) = 0; u(1; t) = 0; (1.1b) u(x; 0) = f(x): (1.1c) The eigenvalues/eigenfunctions are (as calculated in previous sections) 2 2 λn = n π ; φn = sin nπx; n ≥ 1: (1.2) Assuming the solution exists, it can be written in the eigenfunction basis as 1 X u(x; t) = cn(t)φn(x): n=0 Definition (modes) The n-th term of this series is sometimes called the n-th mode or Fourier mode. I'll use the word frequently to describe it (rather than, say, `basis function'). 1 00 Substitute into the PDE (1.1a) and use the fact that −φn = λnφ to obtain 1 X 0 (cn(t) + λncn(t))φn(x) = 0: n=1 By the fact the fφng is a basis, it follows that the coefficient for each mode satisfies the ODE 0 cn(t) + λncn(t) = 0: Solving the ODE gives us a `general' solution to the PDE with its BCs, 1 X −λnt u(x; t) = ane φn(x): n=1 The remaining coefficients are determined by the IC, u(x; 0) = f(x): To match to the solution, we need to also write f(x) in the basis: 1 Z 1 X hf; φni f(x) = f φ (x); f = = 2 f(x) sin nπx dx: (1.3) n n n hφ ; φ i n=1 n n 0 Then from the initial condition, we get u(x; 0) = f(x) 1 1 X X =) cn(0)φn(x) = fnφn(x) n=1 n=1 =) cn(0) = fn for all n ≥ 1: Now everything has been solved - we are done! The solution to the IBVP (1.1) is 1 X −n2π2t u(x; t) = ane sin nπx with an given by (1.5): (1.4) n=1 Alternatively, we could state the solution as follows: The solution is 1 X −λnt u(x; t) = fne φn(x) n=1 with eigenfunctions/values φn; λn given by (1.2) and fn by (1.3). -
Math 356 Lecture Notes Intro to Pdes Eigenfunction Expansions for Ibvps
MATH 356 LECTURE NOTES INTRO TO PDES EIGENFUNCTION EXPANSIONS FOR IBVPS J. WONG (FALL 2019) Topics covered • Eigenfunction expansions for PDEs ◦ The procedure for time-dependent problems ◦ Projection, independent evolution of modes ◦ Separation of variables • The wave equation ◦ Standard solution, standing waves ◦ Example: tuning the vibrating string (resonance) • Interpreting solutions (what does the series mean?) ◦ Relevance of Fourier convergence theorem ◦ Smoothing for the heat equation ◦ Non-smoothing for the wave equation 3 1. The eigenfunction method to solve PDEs We are now ready to demonstrate how to use the components derived thus far to solve the heat equation. The procedure here for the heat equation will extend nicely to a variety of other problems. For now, consider an initial boundary value problem of the form ut = −Lu + h(x; t); x 2 (a; b); t > 0 hom. BCs at a and b (1.1) u(x; 0) = f(x) We seek a solution in terms of the eigenfunction basis X u(x; t) = cn(t)φn(x) n by finding simple ODEs to solve for the coefficients cn(t): This form of the solution is called an eigenfunction expansion for u (or `eigenfunction series') and each term cnφn(x) is a mode (or `Fourier mode' or `eigenmode'). Part 1: find the eigenfunction basis. The first step is to compute the basis. The eigenfunctions we need are the solutions to the eigenvalue problem Lφ = λφ, φ(x) satisfies the BCs for u: (1.2) By the theorem in ??, there is a sequence of eigenfunctions fφng with eigenvalues fλng that form an orthogonal basis for L2[a; b] (i.e. -
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”. -
Sturm-Liouville Expansions of the Delta
Gauge Institute Journal H. Vic Dannon Sturm-Liouville Expansions of the Delta Function H. Vic Dannon [email protected] May, 2014 Abstract We expand the Delta Function in Series, and Integrals of Sturm-Liouville Eigen-functions. Keywords: Sturm-Liouville Expansions, Infinitesimal, Infinite- Hyper-Real, Hyper-Real, infinite Hyper-real, Infinitesimal Calculus, Delta Function, Fourier Series, Laguerre Polynomials, Legendre Functions, Bessel Functions, Delta Function, 2000 Mathematics Subject Classification 26E35; 26E30; 26E15; 26E20; 26A06; 26A12; 03E10; 03E55; 03E17; 03H15; 46S20; 97I40; 97I30. 1 Gauge Institute Journal H. Vic Dannon Contents 0. Eigen-functions Expansion of the Delta Function 1. Hyper-real line. 2. Hyper-real Function 3. Integral of a Hyper-real Function 4. Delta Function 5. Convergent Series 6. Hyper-real Sturm-Liouville Problem 7. Delta Expansion in Non-Normalized Eigen-Functions 8. Fourier-Sine Expansion of Delta Associated with yx"( )+=l yx ( ) 0 & ab==0 9. Fourier-Cosine Expansion of Delta Associated with 1 yx"( )+=l yx ( ) 0 & ab==2 p 10. Fourier-Sine Expansion of Delta Associated with yx"( )+=l yx ( ) 0 & a = 0 11. Fourier-Bessel Expansion of Delta Associated with n2 -1 ux"( )+- (l 4 ) ux ( ) = 0 & ab==0 x 2 12. Delta Expansion in Orthonormal Eigen-functions 13. Fourier-Hermit Expansion of Delta Associated with ux"( )+- (l xux2 ) ( ) = 0 for any real x 14. Fourier-Legendre Expansion of Delta Associated with 2 Gauge Institute Journal H. Vic Dannon 11 11 uu"(ql )++ [ ] ( q ) = 0 -<<pq p 4 cos2 q , 22 15. Fourier-Legendre Expansion of Delta Associated with 112 11 ymy"(ql )++- [ ( ) ] (q ) = 0 -<<pq p 4 cos2 q , 22 16. -
Lecture 15.Pdf
Lecture #15 Eigenfunctions of hermitian operators If the spectrum is discrete , then the eigenfunctions are in Hilbert space and correspond to physically realizable states. It the spectrum is continuous , the eigenfunctions are not normalizable, and they do not correspond to physically realizable states (but their linear combinations may be normalizable). Discrete spectra Normalizable eigenfunctions of a hermitian operator have the following properties: (1) Their eigenvalues are real. (2) Eigenfunctions that belong to different eigenvalues are orthogonal. Finite-dimension vector space: The eigenfunctions of an observable operator are complete , i.e. any function in Hilbert space can be expressed as their linear combination. Continuous spectra Example: Find the eigenvalues and eigenfunctions of the momentum operator . This solution is not square -inferable and operator p has no eigenfunctions in Hilbert space. However, if we only consider real eigenvalues, we can define sort of orthonormality: Lecture 15 Page 1 L15.P2 since the Fourier transform of Dirac delta function is Proof: Plancherel's theorem Now, Then, which looks very similar to orthonormality. We can call such equation Dirac orthonormality. These functions are complete in a sense that any square integrable function can be written in a form and c(p) is obtained by Fourier's trick. Summary for continuous spectra: eigenfunctions with real eigenvalues are Dirac orthonormalizable and complete. Lecture 15 Page 2 L15.P3 Generalized statistical interpretation: If your measure observable Q on a particle in a state you will get one of the eigenvalues of the hermitian operator Q. If the spectrum of Q is discrete, the probability of getting the eigenvalue associated with orthonormalized eigenfunction is It the spectrum is continuous, with real eigenvalues q(z) and associated Dirac-orthonormalized eigenfunctions , the probability of getting a result in the range dz is The wave function "collapses" to the corresponding eigenstate upon measurement. -
Positive Eigenfunctions of Markovian Pricing Operators
Positive Eigenfunctions of Markovian Pricing Operators: Hansen-Scheinkman Factorization, Ross Recovery and Long-Term Pricing Likuan Qin† and Vadim Linetsky‡ Department of Industrial Engineering and Management Sciences McCormick School of Engineering and Applied Sciences Northwestern University Abstract This paper develops a spectral theory of Markovian asset pricing models where the under- lying economic uncertainty follows a continuous-time Markov process X with a general state space (Borel right process (BRP)) and the stochastic discount factor (SDF) is a positive semi- martingale multiplicative functional of X. A key result is the uniqueness theorem for a positive eigenfunction of the pricing operator such that X is recurrent under a new probability mea- sure associated with this eigenfunction (recurrent eigenfunction). As economic applications, we prove uniqueness of the Hansen and Scheinkman (2009) factorization of the Markovian SDF corresponding to the recurrent eigenfunction, extend the Recovery Theorem of Ross (2015) from discrete time, finite state irreducible Markov chains to recurrent BRPs, and obtain the long- maturity asymptotics of the pricing operator. When an asset pricing model is specified by given risk-neutral probabilities together with a short rate function of the Markovian state, we give sufficient conditions for existence of a recurrent eigenfunction and provide explicit examples in a number of important financial models, including affine and quadratic diffusion models and an affine model with jumps. These examples show that the recurrence assumption, in addition to fixing uniqueness, rules out unstable economic dynamics, such as the short rate asymptotically arXiv:1411.3075v3 [q-fin.MF] 9 Sep 2015 going to infinity or to a zero lower bound trap without possibility of escaping. -
Spectral Structure of Transfer Operators for Expanding Circle Maps
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Queen Mary Research Online SPECTRAL STRUCTURE OF TRANSFER OPERATORS FOR EXPANDING CIRCLE MAPS OSCAR F. BANDTLOW, WOLFRAM JUST, AND JULIA SLIPANTSCHUK Abstract. We explicitly determine the spectrum of transfer operators (acting on spaces of holomorphic functions) associated to analytic expanding circle maps arising from finite Blaschke products. This is achieved by deriving a convenient natural representation of the respective adjoint operators. 1. Introduction One of the major strands of modern ergodic theory is to exploit the rich links between dynamical systems theory and functional analysis, making the powerful tools of the latter available for the benefit of understanding complex dynamical be- haviour. In classical ergodic theory, composition operators occur naturally as basic objects for formulating concepts such as ergodicity or mixing [28]. These operators, known in this context as Koopman operators, are the formal adjoints of transfer op- erators, the spectral data of which provide insight into fine statistical properties of the underlying dynamical systems, such as rates of mixing (see, for example, [2, 7]). There exists a large body of literature on spectral estimates for transfer operators (and hence mixing properties) for various one- and higher-dimensional systems with different degree of expansivity or hyperbolicity (see [1] for an overview). However, to the best of our knowledge, no sufficiently rich class of dynamical systems to- gether with a machinery allowing for the explicit analytic determination of the entire spectrum is known. This is the gap the current contribution aims to fill. In the literature, the term `composition operator' is mostly used to refer to compositions with analytic functions mapping a disk into itself, a setting in which operator-theoretic properties such as boundedness, compactness, and most impor- tantly explicit spectral information are well-established (good references are [23] or the encyclopedia on the subject [9]). -
Generalized Eigenfunction Expansions
Universitat¨ Karlsruhe (TH) Fakult¨atf¨urMathematik Institut f¨urAnalysis Arbeitsgruppe Funktionalanalysis Generalized Eigenfunction Expansions Diploma Thesis of Emin Karayel supervised by Prof. Dr. Lutz Weis August 2009 Erkl¨arung Hiermit erkl¨areich, dass ich diese Diplomarbeit selbst¨andigverfasst habe und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Emin Karayel, August 2009 Anschrift Emin Karayel Werner-Siemens-Str. 21 75173 Pforzheim Matr.-Nr.: 1061189 E-Mail: [email protected] 2 Contents 1 Introduction 5 2 Eigenfunction expansions on Hilbert spaces 9 2.1 Spectral Measures . 9 2.2 Phillips Theorem . 11 2.3 Rigged Hilbert Spaces . 12 2.4 Basic Eigenfunction Expansion . 13 2.5 Extending the Operator . 15 2.6 Orthogonalizing Generalized Eigenvectors . 16 2.7 Summary . 21 3 Hilbert-Schmidt-Riggings 23 3.1 Unconditional Hilbert-Schmidt Riggings . 23 3.1.1 Hilbert-Schmidt-Integral Operators . 24 3.2 Weighted Spaces and Sobolev Spaces . 25 3.3 Conditional Hilbert-Schmidt Riggings . 26 3.4 Eigenfunction Expansion for the Laplacian . 27 3.4.1 Optimality . 27 3.5 Nuclear Riggings . 28 4 Operator Relations 31 4.1 Vector valued multiplication operators . 31 4.2 Preliminaries . 33 4.3 Interpolation spaces . 37 4.4 Eigenfunction expansion . 38 4.5 Semigroups . 41 4.6 Expansion for Homogeneous Elliptic Selfadjoint Differential Oper- ators . 42 4.7 Conclusions . 44 5 Appendix 47 5.1 Acknowledgements . 47 5.2 Notation . 47 5.3 Conventions . 49 3 1 Introduction Eigenvectors and eigenvalues are indispensable tools to investigate (at least com- pact) normal linear operators. They provide a diagonalization of the operator i.e.