Properties of Convolution Operators on Lp(0,1)
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
On Singularity Properties of Convolutions of Algebraic Morphisms
ON SINGULARITY PROPERTIES OF CONVOLUTIONS OF ALGEBRAIC MORPHISMS ITAY GLAZER AND YOTAM I. HENDEL Abstract. Let K be a field of characteristic zero, X and Y be smooth K-varieties, and let V be a finite dimensional K-vector space. For two algebraic morphisms ϕ : X → V and ψ : Y → V we define a convolution operation, ϕ ∗ ψ : X × Y → V , by ϕ ∗ ψ(x,y)= ϕ(x)+ ψ(y). We then study the singularity properties of the resulting morphism, and show that as in the case of convolution in analysis, it has improved smoothness properties. Explicitly, we show that for any morphism ϕ : X → V which is dominant when restricted to each irreducible component of X, there exists N ∈ N such that for any n > N the n-th convolution power ϕn := ϕ ∗···∗ ϕ is a flat morphism with reduced geometric fibers of rational singularities (this property is abbreviated (FRS)). By a theorem of Aizenbud and Avni, for K = Q, this is equivalent to good asymptotic behavior of the size of the Z/pkZ-fibers of ϕn when ranging over both p and k. More generally, we show that given a family of morphisms {ϕi : Xi → V } of complexity D ∈ N (i.e. that the number of variables and the degrees of the polynomials defining Xi and ϕi are bounded by D), there exists N(D) ∈ N such that for any n > N(D), the morphism ϕ1 ∗···∗ ϕn is (FRS). Contents 1. Introduction 2 1.1. Motivation 2 1.2. Main results 3 1.3. A brief discussion of the (FRS) property 4 1.4. -
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. -
A Fourier Restriction Theorem Based on Convolution Powers
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 142, Number 11, November 2014, Pages 3897–3901 S 0002-9939(2014)12148-4 Article electronically published on July 21, 2014 A FOURIER RESTRICTION THEOREM BASED ON CONVOLUTION POWERS XIANGHONG CHEN (Communicated by Alexander Iosevich) Abstract. We prove a Fourier restriction estimate under the assumption that certain convolution power of the measure admits an r-integrable density. Introduction Let F be the Fourier transform defined on the Schwartz space by fˆ(ξ)= e−2πiξ,xf(x)dx Rd where ξ,x is the Euclidean inner product. We are interested in Borel measures μ defined on Rd for which F maps Lp(Rd) boundedly to L2(μ), i.e. ˆ d (1) fL2(μ) fLp(Rd), ∀f ∈S(R ). Here “” means the left-hand side is bounded by the right-hand side multiplied by a positive constant that is independent of f. If μ is a singular measure, then such a result can be interpreted as a restriction property of the Fourier transform. Such restriction estimates for singular measures were first obtained by Stein in the 1960’s. If μ is the surface measure on the ≤ ≤ 2(d+1) sphere, the Stein-Tomas theorem [12], [13] states that (1) holds for 1 p d+3 . Mockenhaupt [10] and Mitsis [9] have shown that Tomas’s argument in [12] can be used to obtain an L2-Fourier restriction theorem for a general class of finite Borel measures satisfying (2) |μˆ(ξ)|2 |ξ|−β, ∀ξ ∈ Rd, (3) μ(B(x, r)) rα, ∀x ∈ Rd,r >0, ≤ 4(d−α)+2β where 0 <α,β<d; they showed that (1) holds for 1 p<p0 = 4(d−α)+β .Bak and Seeger [2] proved the same result for the endpoint p0 and further strengthened it by replacing the Lp0 -norm with the Lp0,2-Lorentz norm. -
7. Transformations of Variables
Virtual Laboratories > 2. Distributions > 1 2 3 4 5 6 7 8 7. Transformations of Variables Basic Theory The Problem As usual, we start with a random experiment with probability measure ℙ on an underlying sample space. Suppose that we have a random variable X for the experiment, taking values in S, and a function r:S→T . Then Y=r(X) is a new random variabl e taking values in T . If the distribution of X is known, how do we fin d the distribution of Y ? This is a very basic and important question, and in a superficial sense, the solution is easy. 1. Show that ℙ(Y∈B) = ℙ ( X∈r −1( B)) for B⊆T. However, frequently the distribution of X is known either through its distribution function F or its density function f , and we would similarly like to find the distribution function or density function of Y . This is a difficult problem in general, because as we will see, even simple transformations of variables with simple distributions can lead to variables with complex distributions. We will solve the problem in various special cases. Transformed Variables with Discrete Distributions 2. Suppose that X has a discrete distribution with probability density function f (and hence S is countable). Show that Y has a discrete distribution with probability density function g given by g(y)=∑ f(x), y∈T x∈r −1( y) 3. Suppose that X has a continuous distribution on a subset S⊆ℝ n with probability density function f , and that T is countable. -
Finitary Isomorphisms of Renewal Point Processes and Continuous-Time Regenerative Processes
FINITARY ISOMORPHISMS OF RENEWAL POINT PROCESSES AND CONTINUOUS-TIME REGENERATIVE PROCESSES YINON SPINKA Abstract. We show that a large class of stationary continuous-time regenerative processes are finitarily isomorphic to one another. The key is showing that any stationary renewal point process whose jump distribution is absolutely continuous with exponential tails is finitarily isomorphic to a Poisson point process. We further give simple necessary and sufficient conditions for a renewal point process to be finitarily isomorphic to a Poisson point process. This improves results and answers several questions of Soo [33] and of Kosloff and Soo [19]. 1. Introduction and main results An important problem in ergodic theory is to understand which measure-preserving systems are isomorphic to which other measure-preserving systems. The measure-preserving systems considered here are the flows associated to continuous-time processes and point processes, equipped with the action of R by translations. The simplest and most canonical of these processes are the Poisson point processes.1 It is therefore of particular interest to identify those processes which are isomorphic to a Poisson point process. A consequence of Ornstein theory [25] is that any two Poisson point processes are isomorphic to one another. In fact, Poisson point processes are examples of infinite-entropy Bernoulli flows, and any two infinite-entropy Bernoulli flows are isomorphic. The factor maps provided by Ornstein theory are obtained through abstract existence results. Consequently, such maps are non-explicit and one usually has no control on the coding window, i.e., the size of the window one needs to observe in the source process in order to be able to determine the target process on a fixed interval. -
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. -
Dynamics of Markov Chains for Undergraduates
Dynamics of Markov Chains for Undergraduates Ursula Porod February 19, 2021 Contents Preface 6 1 Markov chains 8 1.1 Introduction . .8 1.2 Construction of a Markov chain . .9 1.2.1 Finite-length trajectories . .9 1.2.2 From finite to infinite-length trajectories . 11 1.3 Basic computations for Markov chains . 16 1.4 Strong Markov Property . 21 1.5 Examples of Markov Chains . 23 1.6 Functions of Markov chains . 35 1.7 Irreducibility and class structure of the state space . 42 1.8 Transience and Recurrence . 44 1.9 Absorbing chains . 53 1.9.1 First step analysis . 53 1.9.2 Finite number of transient states . 55 1.9.3 Infinite number of transient states . 61 1.10 Stationary distributions . 66 1.10.1 Existence and uniqueness of an invariant measure . 68 1.10.2 Positive recurrence versus null recurrence . 73 1.10.3 Stationary distributions for reducible chains . 74 1.10.4 Steady state distributions . 76 1.11 Periodicity . 77 1.12 Exercises . 85 2 Limit Theorems for Markov Chains 87 2.1 The Ergodic Theorem . 87 2.2 Convergence . 93 2.3 Long-run behavior of reducible chains . 98 1 CONTENTS 2 2.4 Exercises . 100 3 Random Walks on Z 101 3.1 Basics . 101 3.2 Wald's Equations . 103 3.3 Gambler's Ruin . 105 3.4 P´olya's Random Walk Theorem . 111 3.5 Reflection Principle and Duality . 115 3.5.1 The ballot problem . 120 3.5.2 Dual walks . 121 3.5.3 Maximum and minimum . -
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. -
Approximation of a Free Poisson Process by Systems of Freely Independent Particles
Approximation of a free Poisson process by systems of freely independent particles Article Accepted Version Boż ejko, M., da Silva, J. L., Kuna, T. and Lytvynov, E. (2018) Approximation of a free Poisson process by systems of freely independent particles. Infinite Dimensional Analysis, Quantum Probability and Related Topics, 21 (3). 1850020. ISSN 1793- 6306 doi: https://doi.org/10.1142/s0219025718500200 Available at http://centaur.reading.ac.uk/78509/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1142/s0219025718500200 Publisher: World Scientific Publishing All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online Approximation of a free Poisson process by systems of freely independent particles Marek Bo_zejko Instytut Matematyczny, Uniwersytet Wroc lawski, Pl. Grunwaldzki 2/4, 50-384 Wroc law, Poland; e-mail: [email protected] Jos´eLu´ısda Silva Universidade da Madeira, Edif´ıcioda Penteada, Caminho da Penteada, 9020-105, Fun- chal, Madeira, Portugal; e-mail: [email protected] Tobias Kuna University of Reading, Department of Mathematics, Whiteknights, PO Box 220, Read- ing RG6 6AX, U.K. e-mail: [email protected] Eugene Lytvynov Department of Mathematics, Swansea University, Singleton Park, Swansea SA2 8PP, U.K.; e-mail: [email protected] Abstract d Let σ be a non-atomic, infinite Radon measure on R , for example, dσ(x) = z dx where z > 0. -
Convolution Semigroups Kevin W
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Louisiana State University Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2009 Convolution semigroups Kevin W. Zito Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Applied Mathematics Commons Recommended Citation Zito, Kevin W., "Convolution semigroups" (2009). LSU Doctoral Dissertations. 2990. https://digitalcommons.lsu.edu/gradschool_dissertations/2990 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. Convolution Semigroups A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Mathematics by Kevin W. Zito B.S. in Mathematics, Louisiana State University, 2001 M.Ed. in Mathematics Education, Louisiana State University, 2002 M.S. in Mathematics, Louisiana State University, 2005 August 2009 Dedication This dissertation is dedicated to the memories of Carl Wayne “Bubba” Belello, Tony Wayne Zito, & Frank Zito ii Acknowledgments It is a great pleasure to thank my advisor Professor Frank Neubrander. This de- gree, and a couple of the other ones, would not have been possible without his guidance and support. I would also like to thank Lee Windsperger, Fareed Hawwa, Dr. -
A Note on the Closure of Convolution Power Mixtures (Random Sums) of Exponential Distributions
J. Aust. Math. Soc. 84 (2008), 1–7 doi:10.1017/S1446788708000104 A NOTE ON THE CLOSURE OF CONVOLUTION POWER MIXTURES (RANDOM SUMS) OF EXPONENTIAL DISTRIBUTIONS J. M. P. ALBIN (Received 11 April 2007; revised 21 July 2007) Communicated by V. T. Stefanov Abstract We make a correction to an important result by Cline [D. B. H. Cline, ‘Convolutions of distributions with exponential tails’, J. Austral. Math. Soc. (Series A) 43 (1987), 347–365; D. B. H. Cline, ‘Convolutions of distributions with exponential tails: corrigendum’, J. Austral. Math. Soc. (Series A) 48 (1990), 152–153] on the closure of the exponential class L(α) under convolution power mixtures (random summation). 2000 Mathematics subject classification: primary 60F99, 62E20; secondary 44A10, 60E07. Keywords and phrases: compound Poisson distribution, convolution power, exponential distribution, random sum, Tauberian theorem. 1. Introduction We say that a cumulative probability distribution function H on the real line belongs to the exponential class L(α), α > 0, if H(x + t) 1 − H(x + t) −αt lim = lim = e for t ∈ R. (1) x→∞ H(x) x→∞ 1 − H(x) (Some authors require members of L(α) to be supported on [0, ∞) to avoid some technicalities. However, because of the applications we have in mind (see, for example, Section 4 below) we do not want to make this restriction.) Note the elementary fact that (1) holds for t ∈ R if and only if it holds for t > 0. Furthermore, H ∈ L(α) if and only if H ◦ log is regularly varying at infinity with index −α. When (1) holds with α = 0 we get the class of long-tailed distributions L(0) = L introduced by Pitman [10]. -
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 .