An Experimental Investigation of the Normality of Irrational Algebraic Numbers
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Quartic Salem Numbers Which Are Mahler Measures of Non-Reciprocal 2-Pisot Numbers Tome 32, No 3 (2020), P
Toufik ZAÏMI Quartic Salem numbers which are Mahler measures of non-reciprocal 2-Pisot numbers Tome 32, no 3 (2020), p. 877-889. <http://jtnb.centre-mersenne.org/item?id=JTNB_2020__32_3_877_0> © Société Arithmétique de Bordeaux, 2020, tous droits réservés. L’accès aux articles de la revue « Journal de Théorie des Nom- bres de Bordeaux » (http://jtnb.centre-mersenne.org/), implique l’accord avec les conditions générales d’utilisation (http://jtnb. centre-mersenne.org/legal/). Toute reproduction en tout ou partie de cet article sous quelque forme que ce soit pour tout usage autre que l’utilisation à fin strictement personnelle du copiste est con- stitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright. cedram Article mis en ligne dans le cadre du Centre de diffusion des revues académiques de mathématiques http://www.centre-mersenne.org/ Journal de Théorie des Nombres de Bordeaux 32 (2020), 877–889 Quartic Salem numbers which are Mahler measures of non-reciprocal 2-Pisot numbers par Toufik ZAÏMI Résumé. Motivé par une question de M. J. Bertin, on obtient des paramé- trisations des polynômes minimaux des nombres de Salem quartiques, disons α, qui sont des mesures de Mahler des 2 -nombres de Pisot non-réciproques. Cela nous permet de déterminer de tels nombres α, de trace donnée, et de déduire que pour tout entier naturel t (resp. t ≥ 2), il y a un nombre de Salem quartique, de trace t, qui est (resp. qui n’est pas) une mesure de Mahler d’un 2 -nombre de Pisot non-réciproque. -
On the Normality of Numbers
ON THE NORMALITY OF NUMBERS Adrian Belshaw B. Sc., University of British Columbia, 1973 M. A., Princeton University, 1976 A THESIS SUBMITTED 'IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in the Department of Mathematics @ Adrian Belshaw 2005 SIMON FRASER UNIVERSITY Fall 2005 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author. APPROVAL Name: Adrian Belshaw Degree: Master of Science Title of Thesis: On the Normality of Numbers Examining Committee: Dr. Ladislav Stacho Chair Dr. Peter Borwein Senior Supervisor Professor of Mathematics Simon Fraser University Dr. Stephen Choi Supervisor Assistant Professor of Mathematics Simon Fraser University Dr. Jason Bell Internal Examiner Assistant Professor of Mathematics Simon Fraser University Date Approved: December 5. 2005 SIMON FRASER ' u~~~snrllbrary DECLARATION OF PARTIAL COPYRIGHT LICENCE The author, whose copyright is declared on the title page of this work, has granted to Simon Fraser University the right to lend this thesis, project or extended essay to users of the Simon Fraser University Library, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational institution, on its own behalf or for one of its users. The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection, and, without changing the content, to translate the thesislproject or extended essays, if technically possible, to any medium or format for the purpose of preservation of the digital work. -
The Number of Solutions of Φ(X) = M
Annals of Mathematics, 150 (1999), 1–29 The number of solutions of φ(x) = m By Kevin Ford Abstract An old conjecture of Sierpi´nskiasserts that for every integer k > 2, there is a number m for which the equation φ(x) = m has exactly k solutions. Here φ is Euler’s totient function. In 1961, Schinzel deduced this conjecture from his Hypothesis H. The purpose of this paper is to present an unconditional proof of Sierpi´nski’sconjecture. The proof uses many results from sieve theory, in particular the famous theorem of Chen. 1. Introduction Of fundamental importance in the theory of numbers is Euler’s totient function φ(n). Two famous unsolved problems concern the possible values of the function A(m), the number of solutions of φ(x) = m, also called the multiplicity of m. Carmichael’s Conjecture ([1],[2]) states that for every m, the equation φ(x) = m has either no solutions x or at least two solutions. In other words, no totient can have multiplicity 1. Although this remains an open problem, very large lower bounds for a possible counterexample are relatively easy to obtain, the latest being 101010 ([10], Th. 6). Recently, the author has also shown that either Carmichael’s Conjecture is true or a positive proportion of all values m of Euler’s function give A(m) = 1 ([10], Th. 2). In the 1950’s, Sierpi´nski conjectured that all multiplicities k > 2 are possible (see [9] and [16]), and in 1961 Schinzel [17] deduced this conjecture from his well-known Hypothesis H [18]. -
PRIME-PERFECT NUMBERS Paul Pollack [email protected] Carl
PRIME-PERFECT NUMBERS Paul Pollack Department of Mathematics, University of Illinois, Urbana, Illinois 61801, USA [email protected] Carl Pomerance Department of Mathematics, Dartmouth College, Hanover, New Hampshire 03755, USA [email protected] In memory of John Lewis Selfridge Abstract We discuss a relative of the perfect numbers for which it is possible to prove that there are infinitely many examples. Call a natural number n prime-perfect if n and σ(n) share the same set of distinct prime divisors. For example, all even perfect numbers are prime-perfect. We show that the count Nσ(x) of prime-perfect numbers in [1; x] satisfies estimates of the form c= log log log x 1 +o(1) exp((log x) ) ≤ Nσ(x) ≤ x 3 ; as x ! 1. We also discuss the analogous problem for the Euler function. Letting N'(x) denote the number of n ≤ x for which n and '(n) share the same set of prime factors, we show that as x ! 1, x1=2 x7=20 ≤ N (x) ≤ ; where L(x) = xlog log log x= log log x: ' L(x)1=4+o(1) We conclude by discussing some related problems posed by Harborth and Cohen. 1. Introduction P Let σ(n) := djn d be the sum of the proper divisors of n. A natural number n is called perfect if σ(n) = 2n and, more generally, multiply perfect if n j σ(n). The study of such numbers has an ancient pedigree (surveyed, e.g., in [5, Chapter 1] and [28, Chapter 1]), but many of the most interesting problems remain unsolved. -
The Smallest Perron Numbers 1
MATHEMATICS OF COMPUTATION Volume 79, Number 272, October 2010, Pages 2387–2394 S 0025-5718(10)02345-8 Article electronically published on April 26, 2010 THE SMALLEST PERRON NUMBERS QIANG WU Abstract. A Perron number is a real algebraic integer α of degree d ≥ 2, whose conjugates are αi, such that α>max2≤i≤d |αi|. In this paper we com- pute the smallest Perron numbers of degree d ≤ 24 and verify that they all satisfy the Lind-Boyd conjecture. Moreover, the smallest Perron numbers of degree 17 and 23 give the smallest house for these degrees. The computa- tions use a family of explicit auxiliary functions. These functions depend on generalizations of the integer transfinite diameter of some compact sets in C 1. Introduction Let α be an algebraic integer of degree d, whose conjugates are α1 = α, α2,...,αd and d d−1 P = X + b1X + ···+ bd−1X + bd, its minimal polynomial. A Perron number, which was defined by Lind [LN1], is a real algebraic integer α of degree d ≥ 2 such that α > max2≤i≤d |αi|.Any Pisot number or Salem number is a Perron number. From the Perron-Frobenius theorem, if A is a nonnegative integral matrix which is aperiodic, i.e. some power of A has strictly positive entries, then its spectral radius α is a Perron number. Lind has proved the converse, that is to say, if α is a Perron number, then there is a nonnegative aperiodic integral matrix whose spectral radius is α.Lind[LN2] has investigated the arithmetic of the Perron numbers. -
Random Number Generation Using Normal Numbers
Random Number Generation Using Normal Numbers Random Number Generation Using Normal Numbers Michael Mascagni1 and F. Steve Brailsford2 Department of Computer Science1;2 Department of Mathematics1 Department of Scientific Computing1 Graduate Program in Molecular Biophysics1 Florida State University, Tallahassee, FL 32306 USA AND Applied and Computational Mathematics Division, Information Technology Laboratory1 National Institute of Standards and Technology, Gaithersburg, MD 20899-8910 USA E-mail: [email protected] or [email protected] or [email protected] URL: http://www.cs.fsu.edu/∼mascagni In collaboration with Dr. David H. Bailey, Lawrence Berkeley Laboratory and UC Davis Random Number Generation Using Normal Numbers Outline of the Talk Introduction Normal Numbers Examples of Normal Numbers Properties Relationship with standard pseudorandom number generators Normal Numbers and Random Number Recursions Normal from recursive sequence Source Code Seed Generation Calculation Code Initial Calculation Code Iteration Implementation and Results Conclusions and Future Work Random Number Generation Using Normal Numbers Introduction Normal Numbers Normal Numbers: Types of Numbers p I Rational numbers - q where p and q are integers I Irrational numbers - not rational I b-dense numbers - α is b-dense () in its base-b expansion every possible finite string of consecutive digits appears I If α is b-dense then α is also irrational; it cannot have a repeating/terminating base-b digit expansion I Normal number - α is b-normal () in its base-b expansion -
Algorithmic Data Analytics, Small Data Matters and Correlation Versus Causation Arxiv:1309.1418V9 [Cs.CE] 26 Jul 2017
Algorithmic Data Analytics, Small Data Matters and Correlation versus Causation∗ Hector Zenil Unit of Computational Medicine, SciLifeLab, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden; Department of Computer Science, University of Oxford, UK; and Algorithmic Nature Group, LABORES, Paris, France. Abstract This is a review of aspects of the theory of algorithmic information that may contribute to a framework for formulating questions related to complex, highly unpredictable systems. We start by contrasting Shannon entropy and Kolmogorov-Chaitin complexity, which epit- omise correlation and causation respectively, and then surveying classical results from algorithmic complexity and algorithmic proba- bility, highlighting their deep connection to the study of automata frequency distributions. We end by showing that though long-range algorithmic prediction models for economic and biological systems may require infinite computation, locally approximated short-range estimations are possible, thereby demonstrating how small data can deliver important insights into important features of complex “Big Data”. arXiv:1309.1418v9 [cs.CE] 26 Jul 2017 Keywords: Correlation; causation; complex systems; algorithmic probability; computability; Kolmogorov-Chaitin complexity. ∗Invited contribution to the festschrift Predictability in the world: philosophy and science in the complex world of Big Data edited by J. Wernecke on the occasion of the retirement of Prof. Dr. Klaus Mainzer, Springer Verlag. Chapter based on an invited talk delivered to UNAM-CEIICH via videoconference from The University of Sheffield in the U.K. for the Alan Turing colloquium “From computers to life” http: //www.complexitycalculator.com/TuringUNAM.pdf) in June, 2012. 1 1 Introduction Complex systems have been studied for some time now, but it was not until recently that sampling complex systems became possible, systems ranging from computational methods such as high-throughput biology, to systems with sufficient storage capacity to store and analyse market transactions. -
Normality and the Digits of Π
Normality and the Digits of π David H. Bailey∗ Jonathan M. Borweiny Cristian S. Caludez Michael J. Dinneenx Monica Dumitrescu{ Alex Yeek February 15, 2012 1 Introduction The question of whether (and why) the digits of well-known constants of mathematics are statistically random in some sense has long fascinated mathematicians. Indeed, one prime motivation in computing and analyzing digits of π is to explore the age-old question of whether and why these digits appear \random." The first computation on ENIAC in 1949 of π to 2037 decimal places was proposed by John von Neumann to shed some light on the distribution of π (and of e)[8, pp. 277{281]. Since then, numerous computer-based statistical checks of the digits of π, for instance, so far have failed to disclose any deviation from reasonable statistical norms. See, for instance, Table1, which presents the counts of individual hexadecimal digits among the first trillion hex digits, as obtained by Yasumasa Kanada. By contrast, ∗Lawrence Berkeley National Laboratory, Berkeley, CA 94720. [email protected]. Supported in part by the Director, Office of Computational and Technology Research, Division of Mathemat- ical, Information, and Computational Sciences of the U.S. Department of Energy, under contract number DE-AC02-05CH11231. yCentre for Computer Assisted Research Mathematics and its Applications (CARMA), Uni- versity of Newcastle, Callaghan, NSW 2308, Australia. [email protected]. Distinguished Professor King Abdul-Aziz Univ, Jeddah. zDepartment of Computer Science, University of Auckland, Private Bag 92019, Auckland, New Zealand. [email protected]. xDepartment of Computer Science, University of Auckland, Private Bag 92019, Auckland, New Zealand. -
Mathematical Constants and Sequences
Mathematical Constants and Sequences a selection compiled by Stanislav Sýkora, Extra Byte, Castano Primo, Italy. Stan's Library, ISSN 2421-1230, Vol.II. First release March 31, 2008. Permalink via DOI: 10.3247/SL2Math08.001 This page is dedicated to my late math teacher Jaroslav Bayer who, back in 1955-8, kindled my passion for Mathematics. Math BOOKS | SI Units | SI Dimensions PHYSICS Constants (on a separate page) Mathematics LINKS | Stan's Library | Stan's HUB This is a constant-at-a-glance list. You can also download a PDF version for off-line use. But keep coming back, the list is growing! When a value is followed by #t, it should be a proven transcendental number (but I only did my best to find out, which need not suffice). Bold dots after a value are a link to the ••• OEIS ••• database. This website does not use any cookies, nor does it collect any information about its visitors (not even anonymous statistics). However, we decline any legal liability for typos, editing errors, and for the content of linked-to external web pages. Basic math constants Binary sequences Constants of number-theory functions More constants useful in Sciences Derived from the basic ones Combinatorial numbers, including Riemann zeta ζ(s) Planck's radiation law ... from 0 and 1 Binomial coefficients Dirichlet eta η(s) Functions sinc(z) and hsinc(z) ... from i Lah numbers Dedekind eta η(τ) Functions sinc(n,x) ... from 1 and i Stirling numbers Constants related to functions in C Ideal gas statistics ... from π Enumerations on sets Exponential exp Peak functions (spectral) .. -
Cryptographic Analysis of Random
CRYPTOGRAPHIC ANALYSIS OF RANDOM SEQUENCES By SANDHYA RANGINENI Bachelor of Technology in Computer Science and Engineering Jawaharlal Nehru Technological University Anantapur, India 2009 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE July, 2011 CRYPTOGRAPHIC ANALYSIS OF RANDOM SEQUENCES Thesis Approved: Dr. Subhash Kak Thesis Adviser Dr. Johnson Thomas Dr. Sanjay Kapil Dr. Mark E. Payton Dean of the Graduate College ii TABLE OF CONTENTS Chapter Page I. INTRODUCTION ......................................................................................................1 1.1 Random Numbers and Random Sequences .......................................................1 1.2 Types of Random Number Generators ..............................................................2 1.3 Random Number Generation Methods ..............................................................3 1.4 Applications of Random Numbers .....................................................................5 1.5 Statistical tests of Random Number Generators ................................................7 II. COMPARISION OF SOME RANDOM SEQUENCES ..........................................9 2.1 Binary Decimal Sequences ................................................................................9 2.1.1 Properties of Decimal Sequences ..........................................................10 • Frequency characteristics .....................................................................10 -
ON the NORMAL NUMBER of PRIME FACTORS of P(N) PAUL
ROCKY MOUNTAIN JOURNAL OF MATHEMATICS Volume IS, Number 2, Spring 1985 ON THE NORMAL NUMBER OF PRIME FACTORS OF p(n) PAUL ERD& AND CARL POMERANCE* Dedicated to the memory of E. G. Straus and R. A. Smith 1. Introduction. Denote by B{rz) the total number of prime factors of n, counting multiplicity, and by w(n) the number of distinct prime factors of n. One of the first results of probabilistic number theory is the theorem of Hardy and Ramanujan that the normal value of w(n) is loglog ~1. What this statement means is that for each E > 0, the set of n for which (1.1) I&l> - loglog nl < E loglog n has asymptotic density 1. The normal value of Q(n) is also loglog n. A paricularly simple proof of these results was later given by Turan. He showed that (13 n$x(w(n) - loglog X)” = X loglog x + O(X) from which (1.1) is an immediate corollary. The method of proof of the asymptotic formula (1.2) was later generalized independently by Turin and Kubilius to give an upper bound for the left hand side where w(n) is replaced by an arbitrary additive function. The significance of the “loglog x” in (1.2) is that it is about CpsX w(p)p-1, where p runs over primes. Similarly the expected value of an arbitrary additive function g(n) should be about CpsX g(p)p-I. The finer distribution of Q(n) and o(n) was studied by many people, culminating in the celebrated Erdb;s-Kac theorem: for each x 2 3, u, let G(x, u) = & * # {?I 5 x: Sd(n) 5 loglog X + U (loglog x)1/2]. -
An Elementary Proof That Almost All Real Numbers Are Normal
Acta Univ. Sapientiae, Mathematica, 2, 1 (2010) 99–110 An elementary proof that almost all real numbers are normal Ferdin´and Filip Jan Sustekˇ Department of Mathematics, Department of Mathematics and Faculty of Education, Institute for Research and J. Selye University, Applications of Fuzzy Modeling, Roln´ıckej ˇskoly 1519, Faculty of Science, 945 01, Kom´arno, Slovakia University of Ostrava, email: [email protected] 30. dubna 22, 701 03 Ostrava 1, Czech Republic email: [email protected] Abstract. A real number is called normal if every block of digits in its expansion occurs with the same frequency. A famous result of Borel is that almost every number is normal. Our paper presents an elementary proof of that fact using properties of a special class of functions. 1 Introduction The concept of normal number was introduced by Borel. A number is called normal if in its base b expansion every block of digits occurs with the same frequency. More exact definition is Definition 1 A real number x ∈ (0,1) is called simply normal to base b ≥ 2 if its base b expansion is 0.c1c2c3 ... and #{n ≤ N | c = a} 1 lim n = for every a ∈ {0,...,b − 1} . N N b 2010 Mathematics→∞ Subject Classification: 11K16, 26A30 Key words and phrases: normal number, measure 99 100 F. Filip, J. Sustekˇ A number is called normal to base b if for every block of digits a1 ...aL, L ≥ 1 #{n ≤ N − L | c = a ,...,c = a } 1 lim n+1 1 n+L L = . N N bL A number is called→∞ absolutely normal if it is normal to every base b ≥ 2.