Randomness in Arithmetic and the Decline and Fall of Reductionism in Pure Mathematics
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Pure Mathematics Professors Teaching and Leading Research CO-OP OR REGULAR 28
PURE MATHEMATICS Pure Mathematics professors teaching and leading research CO-OP OR REGULAR 28 Mathematician ranked among top 10 TOP 10 jobs from 2011-2017 – Comcast.com of grads are employed Search for a deeper 96.6% within 2 years understanding of mathematics Pure mathematics is at the foundation of all mathematical reasoning. If first-year calculus ALEX teaches you how to drive the car, Pure Mathematics teaches you how to build one. 3B, PURE MATHEMATICS AND Mathematicians know that there could be no general relativity without differential COMBINATORICS AND geometry, and no computer security without advanced number theory. OPTIMIZATION Pure Mathematics at Waterloo is a small, cohesive, and challenging program that will open countless doors for you. Our graduates have used the program as a springboard into careers WHAT DO YOU LOVE ABOUT in information technology, finance, business, science, education, and insurance, often by way PURE MATHEMATICS? The satisfaction from understanding of some of the most prestigious graduate programs in the world. an idea at a deeper level and tying together unrelated branches of ALEX’S FAVOURITE COURSES mathematics or physics for the › PMATH 320 Euclidean Geometry: This course is everything you love about Geometry: first time is the most rewarding Euclid’s axioms, isometries of the Euclidean plane and of Euclidean space, polygons, part of learning and understanding polyhedral, polytopes, and the kissing problem. mathematics. What I really enjoy is › PMATH 351 Real Analysis: It’s a very intuitive and natural approach to real analysis, and the developing a deep understanding of complexity of the course builds very naturally to the end of the semester. -
Some First-Order Probability Logics
Theoretical Computer Science 247 (2000) 191–212 www.elsevier.com/locate/tcs View metadata, citation and similar papers at core.ac.uk brought to you by CORE Some ÿrst-order probability logics provided by Elsevier - Publisher Connector Zoran Ognjanovic a;∗, Miodrag RaÄskovic b a MatematickiÄ institut, Kneza Mihaila 35, 11000 Beograd, Yugoslavia b Prirodno-matematickiÄ fakultet, R. Domanovicaà 12, 34000 Kragujevac, Yugoslavia Received June 1998 Communicated by M. Nivat Abstract We present some ÿrst-order probability logics. The logics allow making statements such as P¿s , with the intended meaning “the probability of truthfulness of is greater than or equal to s”. We describe the corresponding probability models. We give a sound and complete inÿnitary axiomatic system for the most general of our logics, while for some restrictions of this logic we provide ÿnitary axiomatic systems. We study the decidability of our logics. We discuss some of the related papers. c 2000 Elsevier Science B.V. All rights reserved. Keywords: First order logic; Probability; Possible worlds; Completeness 1. Introduction In recent years there is a growing interest in uncertainty reasoning. A part of inves- tigation concerns its formal framework – probability logics [1, 2, 5, 6, 9, 13, 15, 17–20]. Probability languages are obtained by adding probability operators of the form (in our notation) P¿s to classical languages. The probability logics allow making formulas such as P¿s , with the intended meaning “the probability of truthfulness of is greater than or equal to s”. Probability models similar to Kripke models are used to give semantics to the probability formulas so that interpreted formulas are either true or false. -
Pure Mathematics
Why Study Mathematics? Mathematics reveals hidden patterns that help us understand the world around us. Now much more than arithmetic and geometry, mathematics today is a diverse discipline that deals with data, measurements, and observations from science; with inference, deduction, and proof; and with mathematical models of natural phenomena, of human behavior, and social systems. The process of "doing" mathematics is far more than just calculation or deduction; it involves observation of patterns, testing of conjectures, and estimation of results. As a practical matter, mathematics is a science of pattern and order. Its domain is not molecules or cells, but numbers, chance, form, algorithms, and change. As a science of abstract objects, mathematics relies on logic rather than on observation as its standard of truth, yet employs observation, simulation, and even experimentation as means of discovering truth. The special role of mathematics in education is a consequence of its universal applicability. The results of mathematics--theorems and theories--are both significant and useful; the best results are also elegant and deep. Through its theorems, mathematics offers science both a foundation of truth and a standard of certainty. In addition to theorems and theories, mathematics offers distinctive modes of thought which are both versatile and powerful, including modeling, abstraction, optimization, logical analysis, inference from data, and use of symbols. Mathematics, as a major intellectual tradition, is a subject appreciated as much for its beauty as for its power. The enduring qualities of such abstract concepts as symmetry, proof, and change have been developed through 3,000 years of intellectual effort. Like language, religion, and music, mathematics is a universal part of human culture. -
Open-Logic-Enderton Rev: C8c9782 (2021-09-28) by OLP/ CC–BY
An Open Mathematical Introduction to Logic Example OLP Text `ala Enderton Open Logic Project open-logic-enderton rev: c8c9782 (2021-09-28) by OLP/ CC{BY Contents I Propositional Logic7 1 Syntax and Semantics9 1.0 Introduction............................ 9 1.1 Propositional Wffs......................... 10 1.2 Preliminaries............................ 11 1.3 Valuations and Satisfaction.................... 12 1.4 Semantic Notions ......................... 14 Problems ................................. 14 2 Derivation Systems 17 2.0 Introduction............................ 17 2.1 The Sequent Calculus....................... 18 2.2 Natural Deduction......................... 19 2.3 Tableaux.............................. 20 2.4 Axiomatic Derivations ...................... 22 3 Axiomatic Derivations 25 3.0 Rules and Derivations....................... 25 3.1 Axiom and Rules for the Propositional Connectives . 26 3.2 Examples of Derivations ..................... 27 3.3 Proof-Theoretic Notions ..................... 29 3.4 The Deduction Theorem ..................... 30 1 Contents 3.5 Derivability and Consistency................... 32 3.6 Derivability and the Propositional Connectives......... 32 3.7 Soundness ............................. 33 Problems ................................. 34 4 The Completeness Theorem 35 4.0 Introduction............................ 35 4.1 Outline of the Proof........................ 36 4.2 Complete Consistent Sets of Sentences ............. 37 4.3 Lindenbaum's Lemma....................... 38 4.4 Construction of a Model -
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. -
B.S. in Mathematics - Pure Math Emphasis, 2020-2021
B.S. in Mathematics - Pure Math Emphasis, 2020-2021 Course Title Cr. Sched. Prerequisites Required Quantitative Lit. MATH 1210 - Calculus I 4 F/S/Su MATH 1050 & 1060 or ACT 26+ GE Course Other GE Courses are required to graduate with a Bachelor’s Degree from SUU.* MATH 1220 Calculus II 4 F/S/Su MATH 1210 MATH 2210 Calculus III 4 F/S/Su MATH 1220 MATH 2270 Linear Algebra 3 F/S/Su MATH 1220 MATH 2280 Differential Equations 3 S MATH 1220 & 2270** Math Core MATH 3120 Transition to Advanced Math 3 F/S MATH 1220 & MATH 2270 Courses MATH 3250 Complex Variables 3 S-Odd MATH 2210 MATH 3700 Probability and Statistics 4 F/S/Su MATH 1220 MATH 4220 Abstract Algebra I 3 F MATH 3120 MATH 4400 Advanced Calculus I 3 F MATH 2210 & 3120 Choose 1 MATH 4230 Abstract Algebra II 3 S-Even MATH 4220 Advanced Class MATH 4410 Advanced Calculus II 3 S-Odd MATH 4400 Choose 1 CS 1400 Fundamentals of Programming 3 F/S CSIS 1030 or MATH 1050 or Permission Program- ming Class CS 1410 Object Oriented Programming 3 F/S/Su CS 1400 MATH 3130 Modern Geometries 3 S MATH 3120 MATH 3160 Number Theory 3 F-Odd MATH 3120 MATH 3500 Actuarial Mathematics 3 S-Even MATH 1100 or MATH 1210 MATH 3600 Numerical Analysis 3 S-Even MATH 2250 or 2280 & comp. knowledge Math MATH 3770 Mathematical Modeling 3 S-Odd MATH 3700 Elective MATH 3800 Partial Differential Equations 3 F-Odd MATH 2210 and 2250 or 2280 Credits (15 total Instructor Permissission; may repeat up MATH 3990 Undergraduate Research 1-3 *** credits to 5 cr. -
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 -
Axiomatic Systems for Geometry∗
Axiomatic Systems for Geometry∗ George Francisy composed 6jan10, adapted 27jan15 1 Basic Concepts An axiomatic system contains a set of primitives and axioms. The primitives are Adaptation to the object names, but the objects they name are left undefined. This is why the primi- current course is in tives are also called undefined terms. The axioms are sentences that make assertions the margins. about the primitives. Such assertions are not provided with any justification, they are neither true nor false. However, every subsequent assertion about the primitives, called a theorem, must be a rigorously logical consequence of the axioms and previ- ously proved theorems. There are also formal definitions in an axiomatic system, but these serve only to simplify things. They establish new object names for complex combinations of primitives and previously defined terms. This kind of definition does not impart any `meaning', not yet, anyway. See Lesson A6 for an elaboration. If, however, a definite meaning is assigned to the primitives of the axiomatic system, called an interpretation, then the theorems become meaningful assertions which might be true or false. If for a given interpretation all the axioms are true, then everything asserted by the theorems also becomes true. Such an interpretation is called a model for the axiomatic system. In common speech, `model' is often used to mean an example of a class of things. In geometry, a model of an axiomatic system is an interpretation of its primitives for ∗From textitPost-Euclidean Geometry: Class Notes and Workbook, UpClose Printing & Copies, Champaign, IL 1995, 2004 yProf. George K. -
Math 310 Class Notes 1: Axioms of Set Theory
MATH 310 CLASS NOTES 1: AXIOMS OF SET THEORY Intuitively, we think of a set as an organization and an element be- longing to a set as a member of the organization. Accordingly, it also seems intuitively clear that (1) when we collect all objects of a certain kind the collection forms an organization, i.e., forms a set, and, moreover, (2) it is natural that when we collect members of a certain kind of an organization, the collection is a sub-organization, i.e., a subset. Item (1) above was challenged by Bertrand Russell in 1901 if we accept the natural item (2), i.e., we must be careful when we use the word \all": (Russell Paradox) The collection of all sets is not a set! Proof. Suppose the collection of all sets is a set S. Consider the subset U of S that consists of all sets x with the property that each x does not belong to x itself. Now since U is a set, U belongs to S. Let us ask whether U belongs to U itself. Since U is the collection of all sets each of which does not belong to itself, thus if U belongs to U, then as an element of the set U we must have that U does not belong to U itself, a contradiction. So, it must be that U does not belong to U. But then U belongs to U itself by the very definition of U, another contradiction. The absurdity results because we assume S is a set. -
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.