Chapter 4 Linear Differential Operators
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Product Integration
Product Integration Richard D. Gill Mathematical Institute, University of Utrecht, Netherlands EURANDOM, Eindhoven, Netherlands May 24, 2001 Abstract This is a brief survey of product-integration for statisticians. All statisticians are familiar with the sum and product symbols and , and with the integral symbol . Also they are aware that there is a certain anal- ogy between summation and integration; in fact the integral symbolPis nothingQ else than a stretched-out capitalR S|the S of summation. Strange therefore that not many people are aware of the existence of the product-integral P, invented by the Italian mathematician Vito Volterra in 1887, which bears exactly the same relation to the ordinary product as the integral does to summation. The mathematical theory of product-integration is not terribly difficult and not terribly deep, which is perhaps one of the reasons it was out of fashion again by the time survival analysis came into being in the fifties. However it is terribly useful and it is a pity that Kaplan and Meier (1958), the inventors of the product-limit or Kaplan-Meier estimator (the nonparametric maximum likelihood estimator of an unknown distribution function based on a sample of censored survival times), did not make the connection, as neither did the au- thors of the classic and papers on this estimator, Efron (1967) and Breslow and Crowley (1974). Only with the Aalen and Johansen (1978) was the connection between the Kaplan-Meier estimator and product-integration made explicit. It took several more years before the connection was put to use to derive new large sample properties of the Kaplan-Meier estimator (e.g., the asymptotic normality of the Kaplan-Meier mean) in Gill (1983). -
Linear Differential Equations in N Th-Order ;
Ordinary Differential Equations Second Course Babylon University 2016-2017 Linear Differential Equations in n th-order ; An nth-order linear differential equation has the form ; …. (1) Where the coefficients ;(j=0,1,2,…,n-1) ,and depends solely on the variable (not on or any derivative of ) If then Eq.(1) is homogenous ,if not then is nonhomogeneous . …… 2 A linear differential equation has constant coefficients if all the coefficients are constants ,if one or more is not constant then has variable coefficients . Examples on linear differential equations ; first order nonhomogeneous second order homogeneous third order nonhomogeneous fifth order homogeneous second order nonhomogeneous Theorem 1: Consider the initial value problem given by the linear differential equation(1)and the n initial Conditions; … …(3) Define the differential operator L(y) by …. (4) Where ;(j=0,1,2,…,n-1) is continuous on some interval of interest then L(y) = and a linear homogeneous differential equation written as; L(y) =0 Definition: Linearly Independent Solution; A set of functions … is linearly dependent on if there exists constants … not all zero ,such that …. (5) on 1 Ordinary Differential Equations Second Course Babylon University 2016-2017 A set of functions is linearly dependent if there exists another set of constants not all zero, that is satisfy eq.(5). If the only solution to eq.(5) is ,then the set of functions is linearly independent on The functions are linearly independent Theorem 2: If the functions y1 ,y2, …, yn are solutions for differential -
The Wronskian
Jim Lambers MAT 285 Spring Semester 2012-13 Lecture 16 Notes These notes correspond to Section 3.2 in the text. The Wronskian Now that we know how to solve a linear second-order homogeneous ODE y00 + p(t)y0 + q(t)y = 0 in certain cases, we establish some theory about general equations of this form. First, we introduce some notation. We define a second-order linear differential operator L by L[y] = y00 + p(t)y0 + q(t)y: Then, a initial value problem with a second-order homogeneous linear ODE can be stated as 0 L[y] = 0; y(t0) = y0; y (t0) = z0: We state a result concerning existence and uniqueness of solutions to such ODE, analogous to the Existence-Uniqueness Theorem for first-order ODE. Theorem (Existence-Uniqueness) The initial value problem 00 0 0 L[y] = y + p(t)y + q(t)y = g(t); y(t0) = y0; y (t0) = z0 has a unique solution on an open interval I containing the point t0 if p, q and g are continuous on I. The solution is twice differentiable on I. Now, suppose that we have obtained two solutions y1 and y2 of the equation L[y] = 0. Then 00 0 00 0 y1 + p(t)y1 + q(t)y1 = 0; y2 + p(t)y2 + q(t)y2 = 0: Let y = c1y1 + c2y2, where c1 and c2 are constants. Then L[y] = L[c1y1 + c2y2] 00 0 = (c1y1 + c2y2) + p(t)(c1y1 + c2y2) + q(t)(c1y1 + c2y2) 00 00 0 0 = c1y1 + c2y2 + c1p(t)y1 + c2p(t)y2 + c1q(t)y1 + c2q(t)y2 00 0 00 0 = c1(y1 + p(t)y1 + q(t)y1) + c2(y2 + p(t)y2 + q(t)y2) = 0: We have just established the following theorem. -
Gsm076-Endmatter.Pdf
http://dx.doi.org/10.1090/gsm/076 Measur e Theor y an d Integratio n This page intentionally left blank Measur e Theor y an d Integratio n Michael E.Taylor Graduate Studies in Mathematics Volume 76 M^^t| American Mathematical Society ^MMOT Providence, Rhode Island Editorial Board David Cox Walter Craig Nikolai Ivanov Steven G. Krantz David Saltman (Chair) 2000 Mathematics Subject Classification. Primary 28-01. For additional information and updates on this book, visit www.ams.org/bookpages/gsm-76 Library of Congress Cataloging-in-Publication Data Taylor, Michael Eugene, 1946- Measure theory and integration / Michael E. Taylor. p. cm. — (Graduate studies in mathematics, ISSN 1065-7339 ; v. 76) Includes bibliographical references. ISBN-13: 978-0-8218-4180-8 1. Measure theory. 2. Riemann integrals. 3. Convergence. 4. Probabilities. I. Title. II. Series. QA312.T387 2006 515/.42—dc22 2006045635 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy a chapter for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. Requests for such permission should be addressed to the Acquisitions Department, American Mathematical Society, 201 Charles Street, Providence, Rhode Island 02904-2294, USA. Requests can also be made by e-mail to [email protected]. © 2006 by the American Mathematical Society. -
Stochastic Integral Equations Without Probability Author(S): Thomas Mikosch and Rimas Norvaiša Source: Bernoulli, Vol
International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics and Probability Stochastic Integral Equations without Probability Author(s): Thomas Mikosch and Rimas Norvaiša Source: Bernoulli, Vol. 6, No. 3 (Jun., 2000), pp. 401-434 Published by: International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics and Probability Stable URL: http://www.jstor.org/stable/3318668 Accessed: 20/04/2010 10:28 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=isibs. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics and Probability is collaborating with JSTOR to digitize, preserve and extend access to Bernoulli. -
An Introduction to Pseudo-Differential Operators
An introduction to pseudo-differential operators Jean-Marc Bouclet1 Universit´ede Toulouse 3 Institut de Math´ematiquesde Toulouse [email protected] 2 Contents 1 Background on analysis on manifolds 7 2 The Weyl law: statement of the problem 13 3 Pseudodifferential calculus 19 3.1 The Fourier transform . 19 3.2 Definition of pseudo-differential operators . 21 3.3 Symbolic calculus . 24 3.4 Proofs . 27 4 Some tools of spectral theory 41 4.1 Hilbert-Schmidt operators . 41 4.2 Trace class operators . 44 4.3 Functional calculus via the Helffer-Sj¨ostrandformula . 50 5 L2 bounds for pseudo-differential operators 55 5.1 L2 estimates . 55 5.2 Hilbert-Schmidt estimates . 60 5.3 Trace class estimates . 61 6 Elliptic parametrix and applications 65 n 6.1 Parametrix on R ................................ 65 6.2 Localization of the parametrix . 71 7 Proof of the Weyl law 75 7.1 The resolvent of the Laplacian on a compact manifold . 75 7.2 Diagonalization of ∆g .............................. 78 7.3 Proof of the Weyl law . 81 A Proof of the Peetre Theorem 85 3 4 CONTENTS Introduction The spirit of these notes is to use the famous Weyl law (on the asymptotic distribution of eigenvalues of the Laplace operator on a compact manifold) as a case study to introduce and illustrate one of the many applications of the pseudo-differential calculus. The material presented here corresponds to a 24 hours course taught in Toulouse in 2012 and 2013. We introduce all tools required to give a complete proof of the Weyl law, mainly the semiclassical pseudo-differential calculus, and then of course prove it! The price to pay is that we avoid presenting many classical concepts or results which are not necessary for our purpose (such as Borel summations, principal symbols, invariance by diffeomorphism or the G˚ardinginequality). -
Linear Differential Operators
3 Linear Differential Operators Differential equations seem to be well suited as models for systems. Thus an understanding of differential equations is at least as important as an understanding of matrix equations. In Section 1.5 we inverted matrices and solved matrix equations. In this chapter we explore the analogous inversion and solution process for linear differential equations. Because of the presence of boundary conditions, the process of inverting a differential operator is somewhat more complex than the analogous matrix inversion. The notation ordinarily used for the study of differential equations is designed for easy handling of boundary conditions rather than for understanding of differential operators. As a consequence, the concept of the inverse of a differential operator is not widely understood among engineers. The approach we use in this chapter is one that draws a strong analogy between linear differential equations and matrix equations, thereby placing both these types of models in the same conceptual frame- work. The key concept is the Green’s function. It plays the same role for a linear differential equation as does the inverse matrix for a matrix equa- tion. There are both practical and theoretical reasons for examining the process of inverting differential operators. The inverse (or integral form) of a differential equation displays explicitly the input-output relationship of the system. Furthermore, integral operators are computationally and theoretically less troublesome than differential operators; for example, differentiation emphasizes data errors, whereas integration averages them. Consequently, the theoretical justification for applying many of the com- putational procedures of later chapters to differential systems is based on the inverse (or integral) description of the system. -
An Extension of Wiener Integration with the Use of Operator Theory
AN EXTENSION OF WIENER INTEGRATION WITH THE USE OF OPERATOR THEORY PALLE E. T. JORGENSEN AND MYUNG-SIN SONG Abstract. With the use of tensor product of Hilbert space, and a diagonal- ization procedure from operator theory, we derive an approximation formula for a general class of stochastic integrals. Further we establish a generalized Fourier expansion for these stochastic integrals. In our extension, we circum- vent some of the limitations of the more widely used stochastic integral due to Wiener and Ito, i.e., stochastic integration with respect to Brownian motion. Finally we discuss the connection between the two approaches, as well as a priori estimates and applications. Contents 1. Introduction 1 2. Notation and Definitions 2 3. Statement of the Main Theorem 3 4. An Application 4 5. Proof of Theorem 3.1 5 5.1. Part A 6 5.2. Part B 7 6. Entropy: Optimal Bases 8 Acknowledgment 12 References 12 1. Introduction arXiv:0901.0195v1 [math-ph] 1 Jan 2009 Recently there has been increase in the number of applications of stochastic inte- gration and stochastic differential equations (SDEs). In addition to the traditional applications in physics and dynamics, stochastic processes have found uses in such areas as option pricing in finance, filtering in signal processing, computations bi- ological models. This fact suggests a need for a widening of the more traditional approach centered about Brownian motion B(t) and Wiener’s integral. Since SDEs are solved with the use of stochastic integrals, we will focus here on integration with respect to a wider class of stochastic processes than has previously been considered. -
First Integrals of Differential Operators from SL(2, R) Symmetries
mathematics Article First Integrals of Differential Operators from SL(2, R) Symmetries Paola Morando 1 , Concepción Muriel 2,* and Adrián Ruiz 3 1 Dipartimento di Scienze Agrarie e Ambientali-Produzione, Territorio, Agroenergia, Università Degli Studi di Milano, 20133 Milano, Italy; [email protected] 2 Departamento de Matemáticas, Facultad de Ciencias, Universidad de Cádiz, 11510 Puerto Real, Spain 3 Departamento de Matemáticas, Escuela de Ingenierías Marina, Náutica y Radioelectrónica, Universidad de Cádiz, 11510 Puerto Real, Spain; [email protected] * Correspondence: [email protected] Received: 30 September 2020; Accepted: 27 November 2020; Published: 4 December 2020 Abstract: The construction of first integrals for SL(2, R)-invariant nth-order ordinary differential equations is a non-trivial problem due to the nonsolvability of the underlying symmetry algebra sl(2, R). Firstly, we provide for n = 2 an explicit expression for two non-constant first integrals through algebraic operations involving the symmetry generators of sl(2, R), and without any kind of integration. Moreover, although there are cases when the two first integrals are functionally independent, it is proved that a second functionally independent first integral arises by a single quadrature. This result is extended for n > 2, provided that a solvable structure for an integrable distribution generated by the differential operator associated to the equation and one of the prolonged symmetry generators of sl(2, R) is known. Several examples illustrate the procedures. Keywords: differential operator; first integral; solvable structure; integrable distribution 1. Introduction The study of nth-order ordinary differential equations admitting the unimodular Lie group SL(2, R) is a non-trivial problem due to the nonsolvability of the underlying symmetry algebra sl(2, R). -
1 Function Spaces, Linear Operators, and Green's Functions
1 1 Function Spaces, Linear Operators, and Green’s Functions 1.1 Function Spaces Consider the set of all complex-valued functions of the real variable x, denoted by f (x), g(x), ..., and defined on the interval (a, b). We shall restrict ourselves to those functions which are square-integrable.Definetheinner product of any two of the latter functions by b ∗ (f , g) ≡ f (x) g (x) dx, (1.1.1) a in which f ∗(x) is the complex conjugate of f (x). The following properties of the inner product follow from definition (1.1.1): (f , g)∗ = (g, f ), ( f , g + h) = ( f , g) + ( f , h), (1.1.2) ( f , αg) = α( f , g), (αf , g) = α∗( f , g), with α a complex scalar. While the inner product of any two functions is in general a complex number, the inner product of a function with itself is a real number and is nonnegative. This prompts us to define the norm of a function by 1 b 2 ∗ f ≡ ( f , f ) = f (x) f (x)dx , (1.1.3) a provided that f is square-integrable , i.e., f < ∞. Equation (1.1.3) constitutes a proper definition for a norm since it satisfies the following conditions: scalar = | | · (i) αf α f , for all comlex α, multiplication (ii) positivity f > 0, for all f = 0, = = f 0, if and only if f 0, triangular (iii) f + g ≤ f + g . inequality (1.1.4) Applied Mathematical Methods in Theoretical Physics, Second Edition. Michio Masujima Copyright 2009 WILEY-VCH Verlag GmbH & Co. -
Sturm Liouville Theory
1 Sturm-Liouville System 1.1 Sturm-Liouville Dierential Equation Denition 1. A second ordered dierential equation of the form d d − p(x) y + q(x)y = λω(x)y; x 2 [a; b] (1) dx dx with p; q and ! specied such that p(x) > 0 and !(x) > 0 for x 2 (a; b), is called a Sturm- Liouville(SL) dierential equation. Note that SL dierential equation is essentially an eigenvalue problem since λ is not specied. While solving SL equation, both λ and y must be determined. Example 2. The Schrodinger equation 2 − ~ + V (x) = E 2m on an interval [a; b] is a SL dierential equation. 1.2 Boundary Conditions Boundary conditions for a solution y of a dierential equation on interval [a; b] are classied as follows: Mixed Boundary Conditions Boundary conditions of the form 0 cay(a) + day (a) = α 0 cby(b) + dby (b) = β (2) where, ca; da; cb; db; α and β are constants, are called mixed Dirichlet-Neumann boundary conditions. When both α = β = 0 the boundary conditions are said to be homogeneous. Special cases are Dirichlet BC (da = db = 0) and Neumann BC (ca = cb = 0) Periodic Boundary Conditions Boundary conditions of the form y(a) = y(b) y0(a) = y0(b) (3) are called periodic boundary conditions. Example 3. Some examples are 1. Stretched vibrating string clamped at two ends: Dirichlet BC 2. Electrostatic potential on the surface of a volume: Dirichlet BC 3. Electrostatic eld on the surface of a volume: Neumann BC 4. A heat-conducting rod with two ends in heat baths: Dirichlet BC 1 1.3 Sturm-Liouville System (Problem) Denition 4. -
Step Functions, Delta Functions, and the Variation of Parameters Formula
STEP FUNCTIONS, DELTA FUNCTIONS, AND THE VARIATION OF PARAMETERS FORMULA STEPHEN SCHECTER 1. The unit step function and piecewise continuous functions The Heaviside unit step function u(t) is given by 0 if t< 0, u(t)= (1 if t> 0. The function u(t) is not defined at t = 0. Often we will not worry about the value of a function at a point where it is discontinuous, since often it doesn’t matter. u(t) u(t−a) 1−u(t−b) u(t−a)−u(t−b) 1 1 1 1 a b a b Figure 1.1. Heaviside unit step function. The function u(t) turns on at t = 0. The function u(t − a) is just u(t) shifted, or dragged, so that it turns on at t = a. The function 1 − u(t − b) turns off at t = b. The function u(t − a) − u(t − b), with a < b, turns on a t = a and turns off at t = b. We can use the unit step function to crop and shift functions. Multiplying f(t) by u(t−a) crops f(t) so that it turns on at t = a: 0 if t < a, u(t − a)f(t)= (f(t) if t > a. Multiplying f(t) by u(t − a) − u(t − b), with a < b, crops f(t) so that it turns on at t = a and turns off at t = b: 0 if t < a, (u(t − a) − u(t − b))f(t)= f(t) if a<t<b, 0 if t > b.