Integrals Exponentials and Logarithms Techniques of Integration
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Conjugate Trigonometric Integrals! by R
SOME THEOREMS ON FOURIER TRANSFORMS AND CONJUGATE TRIGONOMETRIC INTEGRALS! BY R. P. BOAS, JR. 1. Introduction. Functions whose Fourier transforms vanish for large val- ues of the argument have been studied by Paley and Wiener.f It is natural, then, to ask what properties are possessed by functions whose Fourier trans- forms vanish over a finite interval. The two classes of functions are evidently closely related, since a function whose Fourier transform vanishes on ( —A, A) is the difference of two suitably chosen functions, one of which has its Fourier transform vanishing outside (—A, A). In this paper, however, we obtain, for a function having a Fourier transform vanishing over a finite interval, a criterion which does not seem to be trivially related to known results. It is stated in terms of a transform which is closely related to the Hilbert trans- form, and which may be written in the simplest (and probably most interest- ing) case, 1 C" /(* + *) - fix - 0 (1) /*(*) = —- sin t dt. J IT J o t2 With this notation, we shall show that a necessary and sufficient condition for a function/(x) belonging to L2(—<», oo) to have a Fourier transform vanishing almost everywhere on ( — 1, 1) is that/**(x) = —/(*) almost every- where;/**^) is the result of applying the transform (1) to/*(x). The result holds also if (sin t)/t is replaced in the integrand in (1) by a certain more general function Hit). As a preliminary result, we discuss the effect of the general transform/*(x) on a trigonometric Stieltjes integral, (2) /(*) = f e^'dait), J -R where a(/) is a normalized! complex-valued function of bounded variation. -
Numerical Solution of Ordinary Differential Equations
NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS Kendall Atkinson, Weimin Han, David Stewart University of Iowa Iowa City, Iowa A JOHN WILEY & SONS, INC., PUBLICATION Copyright c 2009 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created ore extended by sales representatives or written sales materials. The advice and strategies contained herin may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. -
Numerical Integration
Chapter 12 Numerical Integration Numerical differentiation methods compute approximations to the derivative of a function from known values of the function. Numerical integration uses the same information to compute numerical approximations to the integral of the function. An important use of both types of methods is estimation of derivatives and integrals for functions that are only known at isolated points, as is the case with for example measurement data. An important difference between differen- tiation and integration is that for most functions it is not possible to determine the integral via symbolic methods, but we can still compute numerical approx- imations to virtually any definite integral. Numerical integration methods are therefore more useful than numerical differentiation methods, and are essential in many practical situations. We use the same general strategy for deriving numerical integration meth- ods as we did for numerical differentiation methods: We find the polynomial that interpolates the function at some suitable points, and use the integral of the polynomial as an approximation to the function. This means that the truncation error can be analysed in basically the same way as for numerical differentiation. However, when it comes to round-off error, integration behaves differently from differentiation: Numerical integration is very insensitive to round-off errors, so we will ignore round-off in our analysis. The mathematical definition of the integral is basically via a numerical in- tegration method, and we therefore start by reviewing this definition. We then derive the simplest numerical integration method, and see how its error can be analysed. We then derive two other methods that are more accurate, but for these we just indicate how the error analysis can be done. -
Mathematics 136 – Calculus 2 Summary of Trigonometric Substitutions October 17 and 18, 2016
Mathematics 136 – Calculus 2 Summary of Trigonometric Substitutions October 17 and 18, 2016 The trigonometric substitution method handles many integrals containing expressions like a2 x2, x2 + a2, x2 a2 p − p p − (possibly including expressions without the square roots!) The basis for this approach is the trigonometric identities 1 = sin2 θ + cos2 θ sec2 θ = tan2 θ +1. ⇒ from which we derive other related identities: a2 (a sin θ)2 = a2(1 sin2 θ) = √a2 cos2 θ = a cos θ p − q − (a tan θ)2 + a2 = a2(tan2 θ +1) = √a2 sec2 θ = a sec θ p q (a sec θ)2 a2 = a2(sec2 θ 1) = a2 tan2 θ = a tan θ p − p − p (Technical note: We usually assume that a > 0 and 0 <θ<π/2 here so that all the trig functions take positive values.) Hence, 1. If our integral contains √a2 x2, the substitution x = a sin θ will convert this radical to the simpler form a cos θ. − 2. If our integral contains √x2 + a2, the substitution x = a tan θ will convert this radical to the simpler form a sec θ. 3. If our integral contains √x2 a2, the substitution x = a sec θ will convert this radical to the simpler form a tan θ. − We substitute for the rest of the integral including the dx. For instance if x = a sin θ, then the dx = a cos θ dθ. If x = a tan θ, then dx = a sec2 θ dθ. If x = a sec θ, then dx = a sec θ tan θ dθ. -
On the Numerical Solution of Equations Involving Differential Operators with Constant Coefficients 1
ON THE NUMERICAL SOLUTION OF EQUATIONS 219 The author acknowledges with thanks the aid of Dolores Ufford, who assisted in the calculations. Yudell L. Luke Midwest Research Institute Kansas City 2, Missouri 1 W. E. Milne, "The remainder in linear methods of approximation," NBS, Jn. of Research, v. 43, 1949, p. 501-511. 2W. E. Milne, Numerical Calculus, p. 108-116. 3 M. Bates, On the Development of Some New Formulas for Numerical Integration. Stanford University, June, 1929. 4 M. E. Youngberg, Formulas for Mechanical Quadrature of Irrational Functions. Oregon State College, June, 1937. (The author is indebted to the referee for references 3 and 4.) 6 E. L. Kaplan, "Numerical integration near a singularity," Jn. Math. Phys., v. 26, April, 1952, p. 1-28. On the Numerical Solution of Equations Involving Differential Operators with Constant Coefficients 1. The General Linear Differential Operator. Consider the differential equation of order n (1) Ly + Fiy, x) = 0, where the operator L is defined by j» dky **£**»%- and the functions Pk(x) and Fiy, x) are such that a solution y and its first m derivatives exist in 0 < x < X. In the special case when (1) is linear the solution can be completely determined by the well known method of varia- tion of parameters when n independent solutions of the associated homo- geneous equations are known. Thus for the case when Fiy, x) is independent of y, the solution of the non-homogeneous equation can be obtained by mere quadratures, rather than by laborious stepwise integrations. It does not seem to have been observed, however, that even when Fiy, x) involves the dependent variable y, the numerical integrations can be so arranged that the contributions to the integral from the upper limit at each step of the integration, at the time when y is still unknown at the upper limit, drop out. -
The Original Euler's Calculus-Of-Variations Method: Key
Submitted to EJP 1 Jozef Hanc, [email protected] The original Euler’s calculus-of-variations method: Key to Lagrangian mechanics for beginners Jozef Hanca) Technical University, Vysokoskolska 4, 042 00 Kosice, Slovakia Leonhard Euler's original version of the calculus of variations (1744) used elementary mathematics and was intuitive, geometric, and easily visualized. In 1755 Euler (1707-1783) abandoned his version and adopted instead the more rigorous and formal algebraic method of Lagrange. Lagrange’s elegant technique of variations not only bypassed the need for Euler’s intuitive use of a limit-taking process leading to the Euler-Lagrange equation but also eliminated Euler’s geometrical insight. More recently Euler's method has been resurrected, shown to be rigorous, and applied as one of the direct variational methods important in analysis and in computer solutions of physical processes. In our classrooms, however, the study of advanced mechanics is still dominated by Lagrange's analytic method, which students often apply uncritically using "variational recipes" because they have difficulty understanding it intuitively. The present paper describes an adaptation of Euler's method that restores intuition and geometric visualization. This adaptation can be used as an introductory variational treatment in almost all of undergraduate physics and is especially powerful in modern physics. Finally, we present Euler's method as a natural introduction to computer-executed numerical analysis of boundary value problems and the finite element method. I. INTRODUCTION In his pioneering 1744 work The method of finding plane curves that show some property of maximum and minimum,1 Leonhard Euler introduced a general mathematical procedure or method for the systematic investigation of variational problems. -
Improving Numerical Integration and Event Generation with Normalizing Flows — HET Brown Bag Seminar, University of Michigan —
Improving Numerical Integration and Event Generation with Normalizing Flows | HET Brown Bag Seminar, University of Michigan | Claudius Krause Fermi National Accelerator Laboratory September 25, 2019 In collaboration with: Christina Gao, Stefan H¨oche,Joshua Isaacson arXiv: 191x.abcde Claudius Krause (Fermilab) Machine Learning Phase Space September 25, 2019 1 / 27 Monte Carlo Simulations are increasingly important. https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ComputingandSoftwarePublicResults MC event generation is needed for signal and background predictions. ) The required CPU time will increase in the next years. ) Claudius Krause (Fermilab) Machine Learning Phase Space September 25, 2019 2 / 27 Monte Carlo Simulations are increasingly important. 106 3 10− parton level W+0j 105 particle level W+1j 10 4 W+2j particle level − W+3j 104 WTA (> 6j) W+4j 5 10− W+5j 3 W+6j 10 Sherpa MC @ NERSC Mevt W+7j / 6 Sherpa / Pythia + DIY @ NERSC 10− W+8j 2 10 Frequency W+9j CPUh 7 10− 101 8 10− + 100 W +jets, LHC@14TeV pT,j > 20GeV, ηj < 6 9 | | 10− 1 10− 0 50000 100000 150000 200000 250000 300000 0 1 2 3 4 5 6 7 8 9 Ntrials Njet Stefan H¨oche,Stefan Prestel, Holger Schulz [1905.05120;PRD] The bottlenecks for evaluating large final state multiplicities are a slow evaluation of the matrix element a low unweighting efficiency Claudius Krause (Fermilab) Machine Learning Phase Space September 25, 2019 3 / 27 Monte Carlo Simulations are increasingly important. 106 3 10− parton level W+0j 105 particle level W+1j 10 4 W+2j particle level − W+3j 104 WTA (> -
A Brief Introduction to Numerical Methods for Differential Equations
A Brief Introduction to Numerical Methods for Differential Equations January 10, 2011 This tutorial introduces some basic numerical computation techniques that are useful for the simulation and analysis of complex systems modelled by differential equations. Such differential models, especially those partial differential ones, have been extensively used in various areas from astronomy to biology, from meteorology to finance. However, if we ignore the differences caused by applications and focus on the mathematical equations only, a fundamental question will arise: Can we predict the future state of a system from a known initial state and the rules describing how it changes? If we can, how to make the prediction? This problem, known as Initial Value Problem(IVP), is one of those problems that we are most concerned about in numerical analysis for differential equations. In this tutorial, Euler method is used to solve this problem and a concrete example of differential equations, the heat diffusion equation, is given to demonstrate the techniques talked about. But before introducing Euler method, numerical differentiation is discussed as a prelude to make you more comfortable with numerical methods. 1 Numerical Differentiation 1.1 Basic: Forward Difference Derivatives of some simple functions can be easily computed. However, if the function is too compli- cated, or we only know the values of the function at several discrete points, numerical differentiation is a tool we can rely on. Numerical differentiation follows an intuitive procedure. Recall what we know about the defini- tion of differentiation: df f(x + h) − f(x) = f 0(x) = lim dx h!0 h which means that the derivative of function f(x) at point x is the difference between f(x + h) and f(x) divided by an infinitesimal h. -
Arxiv:1809.06300V1 [Hep-Ph] 17 Sep 2018
CERN-TH-2018-205, TTP18-034 Double-real contribution to the quark beam function at N3LO QCD Kirill Melnikov,1, ∗ Robbert Rietkerk,1, y Lorenzo Tancredi,2, z and Christopher Wever1, 3, x 1Institute for Theoretical Particle Physics, KIT, Karlsruhe, Germany 2Theoretical Physics Department, CERN, 1211 Geneva 23, Switzerland 3Institut f¨urKernphysik, KIT, 76344 Eggenstein-Leopoldshafen, Germany Abstract We compute the master integrals required for the calculation of the double-real emission contri- butions to the matching coefficients of jettiness beam functions at next-to-next-to-next-to-leading order in perturbative QCD. As an application, we combine these integrals and derive the double- real emission contribution to the matching coefficient Iqq(t; z) of the quark beam function. arXiv:1809.06300v1 [hep-ph] 17 Sep 2018 ∗ Electronic address: [email protected] y Electronic address: [email protected] z Electronic address: [email protected] x Electronic address: [email protected] 1 I. INTRODUCTION The absence of any evidence for physics beyond the Standard Model at the LHC implies a growing importance of indirect searches for new particles and interactions. An integral part of this complex endeavour are first-principles predictions for hard scattering processes in proton collisions with controllable perturbative accuracy. In recent years, we have seen a remarkable progress in an effort to provide such predictions. Indeed, robust methods for one-loop computations developed during the past decade, that allowed the theoretical description of a large number of processes with multi-particle final states through NLO QCD [1{6], were followed by the development of practical NNLO QCD subtraction and slicing schemes [7{17] and advances in computations of two-loop scattering amplitudes [18{29]. -
An Analytic Exact Form of the Unit Step Function
Mathematics and Statistics 2(7): 235-237, 2014 http://www.hrpub.org DOI: 10.13189/ms.2014.020702 An Analytic Exact Form of the Unit Step Function J. Venetis Section of Mechanics, Faculty of Applied Mathematics and Physical Sciences, National Technical University of Athens *Corresponding Author: [email protected] Copyright © 2014 Horizon Research Publishing All rights reserved. Abstract In this paper, the author obtains an analytic Meanwhile, there are many smooth analytic exact form of the unit step function, which is also known as approximations to the unit step function as it can be seen in Heaviside function and constitutes a fundamental concept of the literature [4,5,6]. Besides, Sullivan et al [7] obtained a the Operational Calculus. Particularly, this function is linear algebraic approximation to this function by means of a equivalently expressed in a closed form as the summation of linear combination of exponential functions. two inverse trigonometric functions. The novelty of this However, the majority of all these approaches lead to work is that the exact representation which is proposed here closed – form representations consisting of non - elementary is not performed in terms of non – elementary special special functions, e.g. Logistic function, Hyperfunction, or functions, e.g. Dirac delta function or Error function and Error function and also most of its algebraic exact forms are also is neither the limit of a function, nor the limit of a expressed in terms generalized integrals or infinitesimal sequence of functions with point wise or uniform terms, something that complicates the related computational convergence. Therefore it may be much more appropriate in procedures. -
Log-Trigonometric Integrals and Elliptic Functions
Log-trigonometric integrals and elliptic functions Martin Nicholson A class of log-trigonometric integrals are evaluated in terms of elliptic functions. I. INTRODUCTION The following integrals were calculated in [7] π=2 Z a ln x2 + ln2(2e−a cos x) dx = π ln ; (1) eb − 1 0 π=2 Z π 1 1 ln x2 + ln2(2e−a cos x) cos 2x dx = 1 − − eb + ; (2) 2 a eb − 1 0 π=2 Z x sin 2x π 1 eb dx = + eb − ; (3) x2 + ln2(2e−a cos x) 4 a2 (eb − 1)2 0 π=2 γ Z 1 + e2ix π e(γ+1)a dx = − + π H(ln 2 − a); (4) ix − a + ln (2 cos x) a ea − 1 −π=2 where a 2 R, b = minfa; ln 2g, and H is unit step function. These are log-trigonometric integrals of the type whose study was initiated in the series of papers [1{4, 6]. The author of the paper [7] also noted that integral (1) can be used to obtain integrals that can be evaluated in terms of logarithm of Dedekind eta function. However the resulting integrals contained special functions of complex argument. In this paper we modify this approach to obtain integrals of elementary functions of real argument that are evaluated in terms of infinite products or Lambert series. These infinite products and Lambert series can be expressed in terms of elliptic integrals and allow one to obtain closed form evaluation of certain log-trigonometric integrals at particular values of the parameter. In the following we will use standard notations from the theory of elliptic functions. -
Appendix I Integrating Step Functions
Appendix I Integrating Step Functions In Chapter I, section 1, we were considering step functions of the form (1) where II>"" In were brick functions and AI> ... ,An were real coeffi cients. The integral of I was defined by the formula (2) In case of a single real variable, it is almost obvious that the value JI does not depend on the representation (2). In Chapter I, we admitted formula (2) for several variables, relying on analogy only. There are various methods to prove that formula precisely. In this Appendix we are going to give a proof which uses the concept of Heaviside functions. Since the geometrical intuition, above all in multidimensional case, might be decep tive, all the given arguments will be purely algebraic. 1. Heaviside functions The Heaviside function H(x) on the real line Rl admits the value 1 for x;;;o °and vanishes elsewhere; it thus can be defined on writing H(x) = {I for x ;;;0O, ° for x<O. By a Heaviside function H(x) in the plane R2, where x actually denotes points x = (~I> ~2) of R2, we understand the function which admits the value 1 for x ;;;0 0, i.e., for ~l ~ 0, ~2;;;o 0, and vanishes elsewhere. In symbols, H(x) = {I for x ;;;0O, (1.1) ° for x to. Note that we cannot use here the notation x < °(which would mean ~l <0, ~2<0) instead of x~O, because then H(x) would not be defined in the whole plane R 2 • Similarly, in Euclidean q-dimensional space R'l, the Heaviside function H(x), where x = (~I>' .