On the Notions of Upper and Lower Density 3
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Arithmetic Equivalence and Isospectrality
ARITHMETIC EQUIVALENCE AND ISOSPECTRALITY ANDREW V.SUTHERLAND ABSTRACT. In these lecture notes we give an introduction to the theory of arithmetic equivalence, a notion originally introduced in a number theoretic setting to refer to number fields with the same zeta function. Gassmann established a direct relationship between arithmetic equivalence and a purely group theoretic notion of equivalence that has since been exploited in several other areas of mathematics, most notably in the spectral theory of Riemannian manifolds by Sunada. We will explicate these results and discuss some applications and generalizations. 1. AN INTRODUCTION TO ARITHMETIC EQUIVALENCE AND ISOSPECTRALITY Let K be a number field (a finite extension of Q), and let OK be its ring of integers (the integral closure of Z in K). The Dedekind zeta function of K is defined by the Dirichlet series X s Y s 1 ζK (s) := N(I)− = (1 N(p)− )− I OK p − ⊆ where the sum ranges over nonzero OK -ideals, the product ranges over nonzero prime ideals, and N(I) := [OK : I] is the absolute norm. For K = Q the Dedekind zeta function ζQ(s) is simply the : P s Riemann zeta function ζ(s) = n 1 n− . As with the Riemann zeta function, the Dirichlet series (and corresponding Euler product) defining≥ the Dedekind zeta function converges absolutely and uniformly to a nonzero holomorphic function on Re(s) > 1, and ζK (s) extends to a meromorphic function on C and satisfies a functional equation, as shown by Hecke [25]. The Dedekind zeta function encodes many features of the number field K: it has a simple pole at s = 1 whose residue is intimately related to several invariants of K, including its class number, and as with the Riemann zeta function, the zeros of ζK (s) are intimately related to the distribution of prime ideals in OK . -
Measure Theory and Probability Theory
Measure Theory and Probability Theory Stéphane Dupraz In this chapter, we aim at building a theory of probabilities that extends to any set the theory of probability we have for finite sets (with which you are assumed to be familiar). For a finite set with N elements Ω = {ω1, ..., ωN }, a probability P takes any n positive numbers p1, ..., pN that sum to one, and attributes to any subset S of Ω the number (S) = P p . Extending this definition to infinitely countable sets such as P i/ωi∈S i N poses no difficulty: we can in the same way assign a positive number to each integer n ∈ N and require that P∞ 1 P n=1 pn = 1. We can then define the probability of a subset S ⊆ N as P(S) = n∈S pn. Things get more complicated when we move to uncountable sets such as the real line R. To be sure, it is possible to assign a positive number to each real number. But how to get from these positive numbers to the probability of any subset of R?2 To get a definition of a probability that applies without a hitch to uncountable sets, we give in the strategy we used for finite and countable sets and start from scratch. The definition of a probability we are going to use was borrowed from measure theory by Kolmogorov in 1933, which explains the title of this chapter. What do probabilities have to do with measurement? Simple: assigning a probability to an event is measuring the likeliness of this event. -
Density Theorems for Reciprocity Equivalences. Thomas Carrere Palfrey Louisiana State University and Agricultural & Mechanical College
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Louisiana State University Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1989 Density Theorems for Reciprocity Equivalences. Thomas Carrere Palfrey Louisiana State University and Agricultural & Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_disstheses Recommended Citation Palfrey, Thomas Carrere, "Density Theorems for Reciprocity Equivalences." (1989). LSU Historical Dissertations and Theses. 4799. https://digitalcommons.lsu.edu/gradschool_disstheses/4799 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Historical Dissertations and Theses by an authorized administrator of LSU Digital Commons. For more information, please contact [email protected]. INFORMATION TO USERS The most advanced technology has been used to photo graph and reproduce this manuscript from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are re produced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. -
Results Real Analysis I and II, MATH 5453-5463, 2006-2007
Main results Real Analysis I and II, MATH 5453-5463, 2006-2007 Section Homework Introduction. 1.3 Operations with sets. DeMorgan Laws. 1.4 Proposition 1. Existence of the smallest algebra containing C. 2.5 Open and closed sets. 2.6 Continuous functions. Proposition 18. Hw #1. p.16 #9, 11, 17, 18; p.19 #19. 2.7 Borel sets. p.49 #40, 42, 43; p.53 #53*. 3.2 Outer measure. Proposition 1. Outer measure of an interval. Proposition 2. Subadditivity of the outer measure. Proposition 5. Approximation by open sets. 3.3 Measurable sets. Lemma 6. Measurability of sets of outer measure zero. Lemma 7. Measurability of the union. Hw #2. p.55 #1-4; p.58 # 7, 8. Theorem 10. Measurable sets form a sigma-algebra. Lemma 11. Interval is measurable. Theorem 12. Borel sets are measurable. Proposition 13. Sigma additivity of the measure. Proposition 14. Continuity of the measure. Proposition 15. Approximation by open and closed sets. Hw #3. p.64 #9-11, 13, 14. 3.4 A nonmeasurable set. 3.5 Measurable functions. Proposition 18. Equivalent definitions of measurability. Proposition 19. Sums and products of measurable functions. Theorem 20. Infima and suprema of measurable functions. Hw #4. p.70 #18-22. 3.6 Littlewood's three principles. Egoroff's theorem. Lusin's theorem. 4.2 Prop.2. Lebesgue's integral of a simple function and its props. Lebesgue's integral of a bounded measurable function. Proposition 3. Criterion of integrability. Proposition 5. Properties of integrals of bounded functions. Proposition 6. Bounded convergence theorem. 4.3 Lebesgue integral of a nonnegative function and its properties. -
4 L-Functions
Further Number Theory G13FNT cw ’11 4 L-functions In this chapter, we will use analytic tools to study prime numbers. We first start by giving a new proof that there are infinitely many primes and explain what one can say about exactly “how many primes there are”. Then we will use the same tools to proof Dirichlet’s theorem on primes in arithmetic progressions. 4.1 Riemann’s zeta-function and its behaviour at s = 1 Definition. The Riemann zeta function ζ(s) is defined by X 1 1 1 1 1 ζ(s) = = + + + + ··· . ns 1s 2s 3s 4s n>1 The series converges (absolutely) for all s > 1. Remark. The series also converges for complex s provided that Re(s) > 1. 2 4 P 1 Example. From the last chapter, ζ(2) = π /6, ζ(4) = π /90. But ζ(1) is not defined since n diverges. However we can still describe the behaviour of ζ(s) as s & 1 (which is my notation for s → 1+, i.e. s > 1 and s → 1). Proposition 4.1. lim (s − 1) · ζ(s) = 1. s&1 −s R n+1 1 −s Proof. Summing the inequality (n + 1) < n xs dx < n for n > 1 gives Z ∞ 1 1 ζ(s) − 1 < s dx = < ζ(s) 1 x s − 1 and hence 1 < (s − 1)ζ(s) < s; now let s & 1. Corollary 4.2. log ζ(s) lim = 1. s&1 1 log s−1 Proof. Write log ζ(s) log(s − 1)ζ(s) 1 = 1 + 1 log s−1 log s−1 and let s & 1. -
Jeff Connor IDEAL CONVERGENCE GENERATED by DOUBLE
DEMONSTRATIO MATHEMATICA Vol. 49 No 1 2016 Jeff Connor IDEAL CONVERGENCE GENERATED BY DOUBLE SUMMABILITY METHODS Communicated by J. Wesołowski Abstract. The main result of this note is that if I is an ideal generated by a regular double summability matrix summability method T that is the product of two nonnegative regular matrix methods for single sequences, then I-statistical convergence and convergence in I-density are equivalent. In particular, the method T generates a density µT with the additive property (AP) and hence, the additive property for null sets (APO). The densities used to generate statistical convergence, lacunary statistical convergence, and general de la Vallée-Poussin statistical convergence are generated by these types of double summability methods. If a matrix T generates a density with the additive property then T -statistical convergence, convergence in T -density and strong T -summabilty are equivalent for bounded sequences. An example is given to show that not every regular double summability matrix generates a density with additve property for null sets. 1. Introduction The notions of statistical convergence and convergence in density for sequences has been in the literature, under different guises, since the early part of the last century. The underlying generalization of sequential convergence embodied in the definition of convergence in density or statistical convergence has been used in the theory of Fourier analysis, ergodic theory, and number theory, usually in connection with bounded strong summability or convergence in density. Statistical convergence, as it has recently been investigated, was defined by Fast in 1951 [9], who provided an alternate proof of a result of Steinhaus [23]. -
Natural Density and the Quantifier'most'
Natural Density and the Quantifier “Most” Sel¸cuk Topal and Ahmet C¸evik Abstract. This paper proposes a formalization of the class of sentences quantified by most, which is also interpreted as proportion of or ma- jority of depending on the domain of discourse. We consider sentences of the form “Most A are B”, where A and B are plural nouns and the interpretations of A and B are infinite subsets of N. There are two widely used semantics for Most A are B: (i) C(A ∩ B) > C(A \ B) C(A) and (ii) C(A ∩ B) > , where C(X) denotes the cardinality of a 2 given finite set X. Although (i) is more descriptive than (ii), it also produces a considerable amount of insensitivity for certain sets. Since the quantifier most has a solid cardinal behaviour under the interpre- tation majority and has a slightly more statistical behaviour under the interpretation proportional of, we consider an alternative approach in deciding quantity-related statements regarding infinite sets. For this we introduce a new semantics using natural density for sentences in which interpretations of their nouns are infinite subsets of N, along with a list of the axiomatization of the concept of natural density. In other words, we take the standard definition of the semantics of most but define it as applying to finite approximations of infinite sets computed to the limit. Mathematics Subject Classification (2010). 03B65, 03C80, 11B05. arXiv:1901.10394v2 [math.LO] 14 Mar 2019 Keywords. Logic of natural languages; natural density; asymptotic den- sity; arithmetic progression; syllogistic; most; semantics; quantifiers; car- dinality. -
L-Functions and Densities
MATH 776 APPLICATIONS: L-FUNCTIONS AND DENSITIES ANDREW SNOWDEN 1. Dirichlet series P an A Dirichlet series is a series of the form n≥1 ns where the an are complex numbers. Q −s −1 An Euler product is a product of the form p fp(p ) , where the product is over prime numbers p and fp(T ) is a polynomial (called the Euler factor at p) with constant term 1. One can formally expand an Euler product to obtain a Dirichlet series. A Dirichlet series admits an Euler product if and only if an is a multiplicative function of n (i.e., anm = anam for (n; m) = 1) and n 7! apn satisfies a linear recursion for each prime p. P 1 Example 1.1. The Riemann zeta function is ζ(s) = n≥1 ns . It admits the Euler Q −s −1 product ζ(s) = p(1 − p ) . Example 1.2. Let K be a number field. The Dedekind zeta function of K is ζK (s) = P 1 a N(a)−s , where the sum is over all integral ideals a. Note that this is indeed a Dirichlet P an series, as it can be written in the form n≥1 ns where an is the number of ideals of norm n. Q −s −1 It admits the Euler product p(1 − N(p) ) , where the product is over all prime ideals. Q −s −1 Q f(pjp) Note this this can be written as p fp(p ) , where fp(T ) = pjp(1 − T ) and f(pjp) denotes the degree of the residue field extension, and thus indeed fits our definition of Euler product. -
Abelian Group, 521, 526 Absolute Value, 190 Accumulation, Point Of
Index Abelian group, 521, 526 A-set. SeeAnalytic set Absolutevalue, 190 Asymptoticallyequal. 479 Accumulation, point of, 196 Atlas , 231; of holomorphically related Adjoint differentialform, 157, 167 charts, 245 Adjoint operator, 403 Atomic theory, 415 Adjoint space, 397 Automorphism group, 510, 511 Algebra, 524; Boolean, 91, 92; Axiomatic method, in geometry, 507-508 fundamentaltheorem of, 195-196; homo logical, 519-520; normed, 516 BAIREclasses, 460; first, 460, 462, 463; Almost all, 479 of functions, 448 Almost continuous, 460 BAIREcondition, 464 Almost equal, 479 BAIREfunction, 464, 473; non-, 474 Almost everywhere, 70 BAIREspace, 464 Almost linear equation, 321, 323 BAIREsystem, of functions, 459, 460 Alternating differentialform, 185; BAIREtheorem, 448, 460, 462 differentialoperations for, 159-165; BANACH, S., 516 theory of, vi, 143 BANACHfixed point theorem, 423 Alternative theorem, 296, 413 BANACHspace, 338, 340, 393, 399, 432, Analysis, v, 1; axiomaticmethod in, 435,437, 516; adjoint, 400; 512-518; complex, vi ; functional, conjugate, 400; dual, 400; theory of, vi 391; harmonic, 518; and number BANACHtheorem, 446, 447 theory, 500-501 Band spectra, 418 Analytic function, definedby function BAYES theorem, 109 element, 242 BELTRAMIdifferential equation, 325 Analytic numbertheory, 480 BERNOULLI, DANIEL, 23 Analytic operation, 468 BERNOULLI, JACOB, 89, 360 Analytic set, 448, 458, 465, 468, 469; BERNOULLI, JOHANN, 23 linear, 466 BERNOULLIdistribution, 96 Angle-preservingtransformation, 194 BERNOULLIlaw, of large numbers, 116 a-points, -
2.1.3 Outer Measures and Construction of Measures Recall the Construction of Lebesgue Measure on the Real Line
CHAPTER 2. THE LEBESGUE INTEGRAL I 11 2.1.3 Outer measures and construction of measures Recall the construction of Lebesgue measure on the real line. There are various approaches to construct the Lebesgue measure on the real line. For example, consider the collection of finite union of sets of the form ]a; b]; ] − 1; b]; ]a; 1[ R: This collection is an algebra A but not a sigma-algebra (this has been discussed on p.7). A natural measure on A is given by the length of an interval. One attempts to measure the size of any subset S of R covering it by countably many intervals (recall also that R can be covered by countably many intervals) and we define this quantity by 1 1 X [ m∗(S) = inff jAkj : Ak 2 A;S ⊂ Akg (2.7) k=1 k=1 where jAkj denotes the length of the interval Ak. However, m∗(S) is not a measure since it is not additive, which we do not prove here. It is only sigma- subadditive, that is k k [ X m∗ Sn ≤ m∗(Sn) n=1 n=1 for any countable collection (Sn) of subsets of R. Alternatively one could choose coverings by open intervals (see the exercises, see also B.Dacorogna, Analyse avanc´eepour math´ematiciens)Note that the quantity m∗ coincides with the length for any set in A, that is the measure on A (this is surprisingly the more difficult part!). Also note that the sigma-subadditivity implies that m∗(A) = 0 for any countable set since m∗(fpg) = 0 for any p 2 R. -
The Schnirelmann Density of the Set of Deficient Numbers
THE SCHNIRELMANN DENSITY OF THE SET OF DEFICIENT NUMBERS A Thesis Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfillment Of the Requirements for the Degree Master of Science In Mathematics By Peter Gerralld Banda 2015 SIGNATURE PAGE THESIS: THE SCHNIRELMANN DENSITY OF THE SET OF DEFICIENT NUMBERS AUTHOR: Peter Gerralld Banda DATE SUBMITTED: Summer 2015 Mathematics and Statistics Department Dr. Mitsuo Kobayashi Thesis Committee Chair Mathematics & Statistics Dr. Amber Rosin Mathematics & Statistics Dr. John Rock Mathematics & Statistics ii ACKNOWLEDGMENTS I would like to take a moment to express my sincerest appreciation of my fianc´ee’s never ceasing support without which I would not be here. Over the years our rela tionship has proven invaluable and I am sure that there will be many more fruitful years to come. I would like to thank my friends/co-workers/peers who shared the long nights, tears and triumphs that brought my mathematical understanding to what it is today. Without this, graduate school would have been lonely and I prob ably would have not pushed on. I would like to thank my teachers and mentors. Their commitment to teaching and passion for mathematics provided me with the incentive to work and succeed in this educational endeavour. Last but not least, I would like to thank my advisor and mentor, Dr. Mitsuo Kobayashi. Thanks to his patience and guidance, I made it this far with my sanity mostly intact. With his expertise in mathematics and programming, he was able to correct the direction of my efforts no matter how far they strayed. -
A Simple Information Theoretical Proof of the Fueter-Pólya Conjecture
A simple information theoretical proof of the Fueter-P´olya Conjecture Pieter W. Adriaans ILLC, FNWI-IVI, SNE University of Amsterdam, Science Park 107 1098 XG Amsterdam, The Netherlands. Abstract We present a simple information theoretical proof of the Fueter-P´olya Conjec- ture: there is no polynomial pairing function that defines a bijection between the set of natural numbers N and its product set N2 of degree higher than 2. We show that the assumption that such a function exists allows us to con- struct a set of natural numbers that is both compressible and dense. This contradicts a central result of complexity theory that states that the density of the set of compressible numbers is zero in the limit. Keywords: Fueter P´olya Conjecture, Kolmogorov complexity, computational complexity, data structures, theory of computation. 1. Introduction and sketch of the proof The set of natural numbers N can be mapped to its product set by the two so-called Cantor pairing functions π : N2 ! N that defines a two-way polynomial time computable bijection: π(x; y) := 1=2(x + y)(x + y + 1) + y (1) The Fueter - P´olya theorem (Fueter and P´olya (1923)) states that the Cantor pairing function and its symmetric counterpart π0(x; y) = π(y; x) are the only possible quadratic pairing functions. The original proof by Fueter Email address: [email protected] (Pieter W. Adriaans) Preprint submitted to Information Processing Letters January 2, 2018 and P´olya is complex, but a simpler version was published in Vsemirnov (2002) (cf. Nathanson (2016)).