Finite Part Integrals and Hypersingular Kernels
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The Hilbert Transform
(February 14, 2017) The Hilbert transform Paul Garrett [email protected] http:=/www.math.umn.edu/egarrett/ [This document is http:=/www.math.umn.edu/egarrett/m/fun/notes 2016-17/hilbert transform.pdf] 1. The principal-value functional 2. Other descriptions of the principal-value functional 3. Uniqueness of odd/even homogeneous distributions 4. Hilbert transforms 5. Examples 6. ... 7. Appendix: uniqueness of equivariant functionals 8. Appendix: distributions supported at f0g 9. Appendix: the snake lemma Formulaically, the Cauchy principal-value functional η is Z 1 f(x) Z f(x) ηf = principal-value functional of f = P:V: dx = lim dx −∞ x "!0+ jxj>" x This is a somewhat fragile presentation. In particular, the apparent integral is not a literal integral! The uniqueness proven below helps prove plausible properties like the Sokhotski-Plemelj theorem from [Sokhotski 1871], [Plemelji 1908], with a possibly unexpected leading term: Z 1 f(x) Z 1 f(x) lim dx = −iπ f(0) + P:V: dx "!0+ −∞ x + i" −∞ x See also [Gelfand-Silov 1964]. Other plausible identities are best certified using uniqueness, such as Z f(x) − f(−x) ηf = 1 dx (for f 2 C1( ) \ L1( )) 2 x R R R f(x)−f(−x) using the canonical continuous extension of x at 0. Also, 2 Z f(x) − f(0) · e−x ηf = dx (for f 2 Co( ) \ L1( )) x R R R The Hilbert transform of a function f on R is awkwardly described as a principal-value integral 1 Z 1 f(t) 1 Z f(t) (Hf)(x) = P:V: dt = lim dt π −∞ x − t π "!0+ jt−xj>" x − t with the leading constant 1/π understandable with sufficient hindsight: we will see that this adjustment makes H extend to a unitary operator on L2(R). -
An Extension of Hewitt's Inversion Formula and Its Application to Fluctuation Theory
AN EXTENSION OF HEWITT’S INVERSION FORMULA AND ITS APPLICATION TO FLUCTUATION THEORY S¸erban E. B˘adil˘a1 Abstract. We analyze fluctuations of random walks with generally distributed increments. Integral representations for key performance measures are obtained by extending an inversion theorem of Hewitt [11] for Laplace-Stieltjes transforms. Another important step in the analysis involves introducing the so-called harmonic measures associated to the walk. It is also pointed out that such representations can be explicitly calculated, if one assumes a form of rational structure for the increment transform. Applications include, but are not restricted to, queueing and insurance risk problems. Keywords: fluctuations of random walks, harmonic analysis, singular integrals 2010 Mathematics Subject Classification: Primary 60G50 Secondary 30E20 1. Introduction A standard assumption in the theory of fluctuations of random walks as they appear in, e.g., queueing or insurance applications, is that the increment of the random walk can be represented as the difference of two independent random variables. In the context of queueing theory, random walks with increments which do not have this property appear as embedded at arrival epochs of customers in a single server queue in which the service requirement of the current customer is correlated with the time until the next arrival. In risk reserve processes that appear in insurance, the independence assumption is violated when the current claim size depends on the time elapsed since the previous arrival, and hence on the premium gained meanwhile. The purpose of the present paper is to show how much can be done for random walks which do not satisfy this independence assumption, regarding their maxima (as in the waiting time/maximum aggregate loss), their minima (idle periods/deficit at ruin) and their excur- arXiv:1508.00751v1 [math.PR] 4 Aug 2015 sions which are related to busy periods or the time to ruin. -
1 Introduction
Tel Aviv University, 2015 Functions of real variables 1 1 Introduction 1a Integral buyer's guide: know what type is better 1 1b Dimension 2 is a different story . 3 1c Gauge: a bridge from Riemann to Lebesgue . 6 1d Small but dense . 8 1e Sandwich (Riemann), zigzag sandwich (Lebesgue) 9 1f Some achievements of Lebesgue theory . 10 1g Integral or measure? Functions or sets? . 11 This lightweight introduction is not a part of the theory of Lebesgue in- tegration. It is rather a \comparative anatomy" of integration theories1 in- tended to explain what distinguishes Lebesgue's theory from others, and why it is the best but not all-mighty. 1a Integral buyer's guide: know what type is better Several types of integral are well-known: the Riemann integral; ∗ the improper Riemann integral (two types: 1-dim and n-dim); ∗ the Lebesgue integral; ∗ the gauge integral; ∗ the Newton integral (that is, antiderivative). ∗ Probably, the most videly used in practice is the improper Riemann inte- gral; the most well-known to a wide audience is the antiderivative; the most teached are the Riemann and Lebesgue integrals; and the gauge integral is controversial, seldom used, seldom teached, but contains a bright idea able to illuminate the Lebesgue integral. This course is devoted to the Lebesgue integral, but relations to other integrals are also of some interest. For each type of integral we have the definition of integrable function; ∗ the definition of the integral of an integrable function; ∗ theorems on properties of the integral. ∗ 1This phrasing is borrowed from S. Berberian, see the quote on pp. -
Problem Set 4. Generalized Functions
Problem Set 4. GeneralizedProblem functions.set 3 MathMath 212a 212a OctoberSept. 30, 2, 2014,2008 due Due Oct. Oct. 15 1 The purpose of this exercise set, which is mainly computational, is to give you some experience with the ideas associated to generalized functions. Recall that in our study of the Fourier transform we introduced the space consisting of all functions on R which have derivatives of all order which vanishS rapidly at infinity, and that on this space we have a countable family of norms m n f m,n := x D f , ∞ where m and n are non-negative integers. Convergence in means convergence with respect to all of these norms. So a linear functionS on this space is continuous if (fk) 0 whenever fk m,n 0 for every m and n.Welet denote this dual| space,| → the space of continuous → linear functions on . S For example, consider the “Heaviside function” S 1 for x>0 H(x):= . 0 for x 0 ≤ This function does not belong to , but it defines a continuous linear function on by S S ∞ ∞ H, f = f(x)H(x)dx = f(x)dx. 0 −∞ Of course, any element of certainly defines a linear function on by the same procedure, the linear functionS associated to g is just the HilbertS space scalar product ∈ S ∞ f (f,g)= f(x)g(x)dx. → −∞ This also works for any g L2(R), but H L2(R). From the Riesz representa- tion theorem we thus know∈ that the linear function∈ given by H is not continuous with respect to the L2 norm, but it is continuous relative to the topology given above by the countable family of norms . -
Cauchy Principal Value Contour Integral with Applications
entropy Article Cauchy Principal Value Contour Integral with Applications Matilde Legua 1 and Luis M. Sánchez-Ruiz 2,* 1 EINA-Departamento de Matemática Aplicada, Universidad de Zaragoza, Zaragoza 50018, Spain; [email protected] 2 CITG-Departamento de Matemática Aplicada, Universitat Politècnica de València, Valencia 46022, Spain * Correspondence: [email protected]; Tel.: +34-963-877-665 Academic Editors: Carlo Cattani and Kevin Knuth Received: 28 March 2017; Accepted: 3 May 2017; Published: 10 May 2017 Abstract: Cauchy principal value is a standard method applied in mathematical applications by which an improper, and possibly divergent, integral is measured in a balanced way around singularities or at infinity. On the other hand, entropy prediction of systems behavior from a thermodynamic perspective commonly involves contour integrals. With the aim of facilitating the calculus of such integrals in this entropic scenario, we revisit the generalization of Cauchy principal value to complex contour integral, formalize its definition and—by using residue theory techniques—provide an useful way to evaluate them. Keywords: Cauchy principal value; contour integral; entropy as measurement; information extraction; thermodynamics; aerodynamics 1. Introduction Continuous intrinsic entropy treatment requires the assistance of measure theory. This involves adequate integrals intended to evaluate the unexpected uncertainty, indeed this is the case when differential entropy is studied [1–5] in which case improper integrals are commonly used. A number of analytical and numerical methods have been developed such as the Glauert and Goldstein methods developed to study improper integrals that appear in this setting, [6–11], which are relevant in aerodynamics. In fact, aerodynamic entropy study focuses on the overall performance neglecting singularities that enjoy some symmetric entropic phenomena such as turbulence on wing or rotor blades and for this Cauchy principal values are commonly used [12–14]. -
Advanced Mathematical Methods in Theoretical Physics
Advanced Mathematical Methods in Theoretical Physics Wintersemester 2017/2018 Technische Universit¨atBerlin PD Dr. Gernot Schaller March 15, 2021 i This lecture aims at providing physics students and neighboring disciplines with a heuristic toolbox that can be used to tackle all kinds of problems. As time and resources are limited, we will explore only small fractions of different much wider fields, and students are invited to take the lecture only as a starting point. The lecture will try to be as self-contained as possible and aims at providing rather recipes than strict mathematical proofs. It is not an original contribution but an excerpt of many papers, books, other lectures, and own experiences. Only a few of these have been included as references (footnotes describing famous scientists originate from Wikipedia and solely serve for better identification). As successful learning requires practice, the lecture (Mondays) will be accompanied by exer- cises. Students should turn in individual solutions to the exercises at the beginning of Wednesday seminars (computer algebra may be used if applicable). Students may earn points for the exercise sheets, to be admitted to the final exam, they should have earned 50% of the points. After passing the final exam, the students earn in total six ECTS credit points. To get these credit points, students therefore need to get 50 % of the exercise points and 50 % to pass the final exam. The lecture script will be made available online at http://www1.itp.tu-berlin.de/schaller/. Corrections and suggestions for improvements should be addressed to [email protected].