Uniform Asymptotic Expansions of the Incomplete Gamma Functions and the Incomplete Beta Function
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Introduction to Analytic Number Theory the Riemann Zeta Function and Its Functional Equation (And a Review of the Gamma Function and Poisson Summation)
Math 229: Introduction to Analytic Number Theory The Riemann zeta function and its functional equation (and a review of the Gamma function and Poisson summation) Recall Euler’s identity: ∞ ∞ X Y X Y 1 [ζ(s) :=] n−s = p−cps = . (1) 1 − p−s n=1 p prime cp=0 p prime We showed that this holds as an identity between absolutely convergent sums and products for real s > 1. Riemann’s insight was to consider (1) as an identity between functions of a complex variable s. We follow the curious but nearly universal convention of writing the real and imaginary parts of s as σ and t, so s = σ + it. We already observed that for all real n > 0 we have |n−s| = n−σ, because n−s = exp(−s log n) = n−σe−it log n and e−it log n has absolute value 1; and that both sides of (1) converge absolutely in the half-plane σ > 1, and are equal there either by analytic continuation from the real ray t = 0 or by the same proof we used for the real case. Riemann showed that the function ζ(s) extends from that half-plane to a meromorphic function on all of C (the “Riemann zeta function”), analytic except for a simple pole at s = 1. The continuation to σ > 0 is readily obtained from our formula ∞ ∞ 1 X Z n+1 X Z n+1 ζ(s) − = n−s − x−s dx = (n−s − x−s) dx, s − 1 n=1 n n=1 n since for x ∈ [n, n + 1] (n ≥ 1) and σ > 0 we have Z x −s −s −1−s −1−σ |n − x | = s y dy ≤ |s|n n so the formula for ζ(s) − (1/(s − 1)) is a sum of analytic functions converging absolutely in compact subsets of {σ + it : σ > 0} and thus gives an analytic function there. -
The Error Function Mathematical Physics
R. I. Badran The Error Function Mathematical Physics The Error Function and Stirling’s Formula The Error Function: x 2 The curve of the Gaussian function y e is called the bell-shaped graph. The error function is defined as the area under part of this curve: x 2 2 erf (x) et dt 1. . 0 There are other definitions of error functions. These are closely related integrals to the above one. 2. a) The normal or Gaussian distribution function. x t2 1 1 1 x P(, x) e 2 dt erf ( ) 2 2 2 2 Proof: Put t 2u and proceed, you might reach a step of x 1 2 P(0, x) eu du P(,x) P(,0) P(0,x) , where 0 1 x P(0, x) erf ( ) Here you can prove that 2 2 . This can be done by using the definition of error function in (1). 0 u2 I I e du Now you need to find P(,0) where . To find this integral you have to put u=x first, then u= y and multiply the two resulting integrals. Make the change of variables to polar coordinate you get R. I. Badran The Error Function Mathematical Physics 0 2 2 I 2 er rdr d 0 From this latter integral you get 1 I P(,0) 2 and 2 . 1 1 x P(, x) erf ( ) 2 2 2 Q. E. D. x 2 t 1 2 1 x 2.b P(0, x) e dt erf ( ) 2 0 2 2 (as proved earlier in 2.a). -
Probabilistic Proofs of Some Beta-Function Identities
1 2 Journal of Integer Sequences, Vol. 22 (2019), 3 Article 19.6.6 47 6 23 11 Probabilistic Proofs of Some Beta-Function Identities Palaniappan Vellaisamy Department of Mathematics Indian Institute of Technology Bombay Powai, Mumbai-400076 India [email protected] Aklilu Zeleke Lyman Briggs College & Department of Statistics and Probability Michigan State University East Lansing, MI 48825 USA [email protected] Abstract Using a probabilistic approach, we derive some interesting identities involving beta functions. These results generalize certain well-known combinatorial identities involv- ing binomial coefficients and gamma functions. 1 Introduction There are several interesting combinatorial identities involving binomial coefficients, gamma functions, and hypergeometric functions (see, for example, Riordan [9], Bagdasaryan [1], 1 Vellaisamy [15], and the references therein). One of these is the following famous identity that involves the convolution of the central binomial coefficients: n 2k 2n − 2k =4n. (1) k n − k k X=0 In recent years, researchers have provided several proofs of (1). A proof that uses generating functions can be found in Stanley [12]. Combinatorial proofs can also be found, for example, in Sved [13], De Angelis [3], and Miki´c[6]. A related and an interesting identity for the alternating convolution of central binomial coefficients is n n n 2k 2n − 2k 2 n , if n is even; (−1)k = 2 (2) k n − k k=0 (0, if n is odd. X Nagy [7], Spivey [11], and Miki´c[6] discussed the combinatorial proofs of the above identity. Recently, there has been considerable interest in finding simple probabilistic proofs for com- binatorial identities (see Vellaisamy and Zeleke [14] and the references therein). -
Asymptotic Estimates of Stirling Numbers
Asymptotic Estimates of Stirling Numbers By N. M. Temme New asymptotic estimates are given of the Stirling numbers s~mJ and (S)~ml, of first and second kind, respectively, as n tends to infinity. The approxima tions are uniformly valid with respect to the second parameter m. 1. Introduction The Stirling numbers of the first and second kind, denoted by s~ml and (S~~m>, respectively, are defined through the generating functions n x(x-l)···(x-n+l) = [. S~m)Xm, ( 1.1) m= 0 n L @~mlx(x -1) ··· (x - m + 1), ( 1.2) m= 0 where the left-hand side of (1.1) has the value 1 if n = 0; similarly, for the factors in the right-hand side of (1.2) if m = 0. This gives the 'boundary values' Furthermore, it is convenient to agree on s~ml = @~ml= 0 if m > n. The Stirling numbers are integers; apart from the above mentioned zero values, the numbers of the second kind are positive; those of the first kind have the sign of ( - l)n + m. Address for correspondence: Professor N. M. Temme, CWI, P.O. Box 4079, 1009 AB Amsterdam, The Netherlands. e-mail: [email protected]. STUDIES IN APPLIED MATHEMATICS 89:233-243 (1993) 233 Copyright © 1993 by the Massachusetts Institute of Technology Published by Elsevier Science Publishing Co., Inc. 0022-2526 /93 /$6.00 234 N. M. Temme Alternative generating functions are [ln(x+1)r :x n " s(ln)~ ( 1.3) m! £..,, n n!' n=m ( 1.4) The Stirling numbers play an important role in difference calculus, combina torics, and probability theory. -
Error Functions
Error functions Nikolai G. Lehtinen April 23, 2010 1 Error function erf x and complementary er- ror function erfc x (Gauss) error function is 2 x 2 erf x = e−t dt (1) √π Z0 and has properties erf ( )= 1, erf (+ ) = 1 −∞ − ∞ erf ( x)= erf (x), erf (x∗) = [erf(x)]∗ − − where the asterisk denotes complex conjugation. Complementary error function is defined as ∞ 2 2 erfc x = e−t dt = 1 erf x (2) √π Zx − Note also that 2 x 2 e−t dt = 1 + erf x −∞ √π Z Another useful formula: 2 x − t π x e 2σ2 dt = σ erf Z0 r 2 "√2σ # Some Russian authors (e.g., Mikhailovskiy, 1975; Bogdanov et al., 1976) call erf x a Cramp function. 1 2 Faddeeva function w(x) Faddeeva (or Fadeeva) function w(x)(Fadeeva and Terent’ev, 1954; Poppe and Wijers, 1990) does not have a name in Abramowitz and Stegun (1965, ch. 7). It is also called complex error function (or probability integral)(Weide- man, 1994; Baumjohann and Treumann, 1997, p. 310) or plasma dispersion function (Weideman, 1994). To avoid confusion, we will reserve the last name for Z(x), see below. Some Russian authors (e.g., Mikhailovskiy, 1975; Bogdanov et al., 1976) call it a (complex) Cramp function and denote as W (x). Faddeeva function is defined as 2 2i x 2 2 2 w(x)= e−x 1+ et dt = e−x [1+erf(ix)] = e−x erfc ( ix) (3) √π Z0 ! − Integral representations: 2 2 i ∞ e−t dt 2ix ∞ e−t dt w(x)= = (4) π −∞ x t π 0 x2 t2 Z − Z − where x> 0. -
INTEGRALS of POWERS of LOGGAMMA 1. Introduction The
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 139, Number 2, February 2011, Pages 535–545 S 0002-9939(2010)10589-0 Article electronically published on August 18, 2010 INTEGRALS OF POWERS OF LOGGAMMA TEWODROS AMDEBERHAN, MARK W. COFFEY, OLIVIER ESPINOSA, CHRISTOPH KOUTSCHAN, DANTE V. MANNA, AND VICTOR H. MOLL (Communicated by Ken Ono) Abstract. Properties of the integral of powers of log Γ(x) from 0 to 1 are con- sidered. Analytic evaluations for the first two powers are presented. Empirical evidence for the cubic case is discussed. 1. Introduction The evaluation of definite integrals is a subject full of interconnections of many parts of mathematics. Since the beginning of Integral Calculus, scientists have developed a large variety of techniques to produce magnificent formulae. A partic- ularly beautiful formula due to J. L. Raabe [12] is 1 Γ(x + t) (1.1) log √ dx = t log t − t, for t ≥ 0, 0 2π which includes the special case 1 √ (1.2) L1 := log Γ(x) dx =log 2π. 0 Here Γ(x)isthegamma function defined by the integral representation ∞ (1.3) Γ(x)= ux−1e−udu, 0 for Re x>0. Raabe’s formula can be obtained from the Hurwitz zeta function ∞ 1 (1.4) ζ(s, q)= (n + q)s n=0 via the integral formula 1 t1−s (1.5) ζ(s, q + t) dq = − 0 s 1 coupled with Lerch’s formula ∂ Γ(q) (1.6) ζ(s, q) =log √ . ∂s s=0 2π An interesting extension of these formulas to the p-adic gamma function has recently appeared in [3]. -
Global Minimax Approximations and Bounds for the Gaussian Q-Functionbysumsofexponentials 3
IEEE TRANSACTIONS ON COMMUNICATIONS 1 Global Minimax Approximations and Bounds for the Gaussian Q-Function by Sums of Exponentials Islam M. Tanash and Taneli Riihonen , Member, IEEE Abstract—This paper presents a novel systematic methodology The Gaussian Q-function has many applications in statis- to obtain new simple and tight approximations, lower bounds, tical performance analysis such as evaluating bit, symbol, and upper bounds for the Gaussian Q-function, and functions and block error probabilities for various digital modulation thereof, in the form of a weighted sum of exponential functions. They are based on minimizing the maximum absolute or relative schemes and different fading models [5]–[11], and evaluat- error, resulting in globally uniform error functions with equalized ing the performance of energy detectors for cognitive radio extrema. In particular, we construct sets of equations that applications [12], [13], whenever noise and interference or describe the behaviour of the targeted error functions and a channel can be modelled as a Gaussian random variable. solve them numerically in order to find the optimized sets of However, in many cases formulating such probabilities will coefficients for the sum of exponentials. This also allows for establishing a trade-off between absolute and relative error by result in complicated integrals of the Q-function that cannot controlling weights assigned to the error functions’ extrema. We be expressed in a closed form in terms of elementary functions. further extend the proposed procedure to derive approximations Therefore, finding tractable approximations and bounds for and bounds for any polynomial of the Q-function, which in the Q-function becomes a necessity in order to facilitate turn allows approximating and bounding many functions of the expression manipulations and enable its application over a Q-function that meet the Taylor series conditions, and consider the integer powers of the Q-function as a special case. -
Standard Distributions
Appendix A Standard Distributions A.1 Standard Univariate Discrete Distributions I. Binomial Distribution B(n, p) has the probability mass function n f(x)= px (x =0, 1,...,n). x The mean is np and variance np(1 − p). II. The Negative Binomial Distribution NB(r, p) arises as the distribution of X ≡{number of failures until the r-th success} in independent trials each with probability of success p. Thus its probability mass function is r + x − 1 r x fr(x)= p (1 − p) (x =0, 1, ··· , ···). x Let Xi denote the number of failures between the (i − 1)-th and i-th successes (i =2, 3,...,r), and let X1 be the number of failures before the first success. Then X1,X2,...,Xr and r independent random variables each having the distribution NB(1,p) with probability mass function x f1(x)=p(1 − p) (x =0, 1, 2,...). Also, X = X1 + X2 + ···+ Xr. Hence ∞ ∞ x x−1 E(X)=rEX1 = r p x(1 − p) = rp(1 − p) x(1 − p) x=0 x=1 ∞ ∞ d d = rp(1 − p) − (1 − p)x = −rp(1 − p) (1 − p)x x=1 dp dp x=1 © Springer-Verlag New York 2016 343 R. Bhattacharya et al., A Course in Mathematical Statistics and Large Sample Theory, Springer Texts in Statistics, DOI 10.1007/978-1-4939-4032-5 344 A Standard Distributions ∞ d d 1 = −rp(1 − p) (1 − p)x = −rp(1 − p) dp dp 1 − (1 − p) x=0 =p r(1 − p) = . (A.1) p Also, one may calculate var(X) using (Exercise A.1) var(X)=rvar(X1). -
Asymptotics of Singularly Perturbed Volterra Type Integro-Differential Equation
Konuralp Journal of Mathematics, 8 (2) (2020) 365-369 Konuralp Journal of Mathematics Journal Homepage: www.dergipark.gov.tr/konuralpjournalmath e-ISSN: 2147-625X Asymptotics of Singularly Perturbed Volterra Type Integro-Differential Equation Fatih Say1 1Department of Mathematics, Faculty of Arts and Sciences, Ordu University, Ordu, Turkey Abstract This paper addresses the asymptotic behaviors of a linear Volterra type integro-differential equation. We study a singular Volterra integro equation in the limiting case of a small parameter with proper choices of the unknown functions in the equation. We show the effectiveness of the asymptotic perturbation expansions with an instructive model equation by the methods in superasymptotics. The methods used in this study are also valid to solve some other Volterra type integral equations including linear Volterra integro-differential equations, fractional integro-differential equations, and system of singular Volterra integral equations involving small (or large) parameters. Keywords: Singular perturbation, Volterra integro-differential equations, asymptotic analysis, singularity 2010 Mathematics Subject Classification: 41A60; 45M05; 34E15 1. Introduction A systematic approach to approximation theory can find in the subject of asymptotic analysis which deals with the study of problems in the appropriate limiting regimes. Approximations of some solutions of differential equations, usually containing a small parameter e, are essential in the analysis. The subject has made tremendous growth in recent years and has a vast literature. Developed techniques of asymptotics are successfully applied to problems including the classical long-standing problems in mathematics, physics, fluid me- chanics, astrodynamics, engineering and many diverse fields, for instance, see [1, 2, 3, 4, 5, 6, 7]. There exists a plethora of examples of asymptotics which includes a wide variety of problems in rich results from the very practical to the highly theoretical analysis. -
An Introduction to Asymptotic Analysis Simon JA Malham
An introduction to asymptotic analysis Simon J.A. Malham Department of Mathematics, Heriot-Watt University Contents Chapter 1. Order notation 5 Chapter 2. Perturbation methods 9 2.1. Regular perturbation problems 9 2.2. Singular perturbation problems 15 Chapter 3. Asymptotic series 21 3.1. Asymptotic vs convergent series 21 3.2. Asymptotic expansions 25 3.3. Properties of asymptotic expansions 26 3.4. Asymptotic expansions of integrals 29 Chapter 4. Laplace integrals 31 4.1. Laplace's method 32 4.2. Watson's lemma 36 Chapter 5. Method of stationary phase 39 Chapter 6. Method of steepest descents 43 Bibliography 49 Appendix A. Notes 51 A.1. Remainder theorem 51 A.2. Taylor series for functions of more than one variable 51 A.3. How to determine the expansion sequence 52 A.4. How to find a suitable rescaling 52 Appendix B. Exam formula sheet 55 3 CHAPTER 1 Order notation The symbols , o and , were first used by E. Landau and P. Du Bois- Reymond and areOdefined as∼ follows. Suppose f(z) and g(z) are functions of the continuous complex variable z defined on some domain C and possess D ⊂ limits as z z0 in . Then we define the following shorthand notation for the relative!propertiesD of these functions in the limit z z . ! 0 Asymptotically bounded: f(z) = (g(z)) as z z ; O ! 0 means that: there exists constants K 0 and δ > 0 such that, for 0 < z z < δ, ≥ j − 0j f(z) K g(z) : j j ≤ j j We say that f(z) is asymptotically bounded by g(z) in magnitude as z z0, or more colloquially, and we say that f(z) is of `order big O' of g(z). -
A Note on the Asymptotic Expansion of the Lerch's Transcendent Arxiv
A note on the asymptotic expansion of the Lerch’s transcendent Xing Shi Cai∗1 and José L. Lópezy2 1Department of Mathematics, Uppsala University, Uppsala, Sweden 2Departamento de Estadística, Matemáticas e Informática and INAMAT, Universidad Pública de Navarra, Pamplona, Spain June 5, 2018 Abstract In [7], the authors derived an asymptotic expansion of the Lerch’s transcendent Φ(z; s; a) for large jaj, valid for <a > 0, <s > 0 and z 2 C n [1; 1). In this paper we study the special case z ≥ 1 not covered in [7], deriving a complete asymptotic expansion of the Lerch’s transcendent Φ(z; s; a) for z > 1 and <s > 0 as <a goes to infinity. We also show that when a is a positive integer, this expansion is convergent for Pm n s <z ≥ 1. As a corollary, we get a full asymptotic expansion for the sum n=1 z =n for fixed z > 1 as m ! 1. Some numerical results show the accuracy of the approximation. 1 Introduction The Lerch’s transcendent (Hurwitz-Lerch zeta function) [2, §25.14(i)] is defined by means of the power series 1 X zn Φ(z; s; a) = ; a 6= 0; −1; −2;:::; (a + n)s n=0 on the domain jzj < 1 for any s 2 C or jzj ≤ 1 for <s > 1. For other values of the variables arXiv:1806.01122v1 [math.CV] 4 Jun 2018 z; s; a, the function Φ(z; s; a) is defined by analytic continuation. In particular [7], 1 Z 1 xs−1e−ax Φ(z; s; a) = −x dx; <a > 0; z 2 C n [1; 1) and <s > 0: (1) Γ(s) 0 1 − ze ∗This work is supported by the Knut and Alice Wallenberg Foundation and the Ministerio de Economía y Competitividad of the spanish government (MTM2017-83490-P). -
Sums of Powers and the Bernoulli Numbers Laura Elizabeth S
Eastern Illinois University The Keep Masters Theses Student Theses & Publications 1996 Sums of Powers and the Bernoulli Numbers Laura Elizabeth S. Coen Eastern Illinois University This research is a product of the graduate program in Mathematics and Computer Science at Eastern Illinois University. Find out more about the program. Recommended Citation Coen, Laura Elizabeth S., "Sums of Powers and the Bernoulli Numbers" (1996). Masters Theses. 1896. https://thekeep.eiu.edu/theses/1896 This is brought to you for free and open access by the Student Theses & Publications at The Keep. It has been accepted for inclusion in Masters Theses by an authorized administrator of The Keep. For more information, please contact [email protected]. THESIS REPRODUCTION CERTIFICATE TO: Graduate Degree Candidates (who have written formal theses) SUBJECT: Permission to Reproduce Theses The University Library is rece1v1ng a number of requests from other institutions asking permission to reproduce dissertations for inclusion in their library holdings. Although no copyright laws are involved, we feel that professional courtesy demands that permission be obtained from the author before we allow theses to be copied. PLEASE SIGN ONE OF THE FOLLOWING STATEMENTS: Booth Library of Eastern Illinois University has my permission to lend my thesis to a reputable college or university for the purpose of copying it for inclusion in that institution's library or research holdings. u Author uate I respectfully request Booth Library of Eastern Illinois University not allow my thesis