Arithmetic Equivalence and Isospectrality
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NOTES on FINITE GROUP REPRESENTATIONS in Fall 2020, I
NOTES ON FINITE GROUP REPRESENTATIONS CHARLES REZK In Fall 2020, I taught an undergraduate course on abstract algebra. I chose to spend two weeks on the theory of complex representations of finite groups. I covered the basic concepts, leading to the classification of representations by characters. I also briefly addressed a few more advanced topics, notably induced representations and Frobenius divisibility. I'm making the lectures and these associated notes for this material publicly available. The material here is standard, and is mainly based on Steinberg, Representation theory of finite groups, Ch 2-4, whose notation I will mostly follow. I also used Serre, Linear representations of finite groups, Ch 1-3.1 1. Group representations Given a vector space V over a field F , we write GL(V ) for the group of bijective linear maps T : V ! V . n n When V = F we can write GLn(F ) = GL(F ), and identify the group with the group of invertible n × n matrices. A representation of a group G is a homomorphism of groups φ: G ! GL(V ) for some representation choice of vector space V . I'll usually write φg 2 GL(V ) for the value of φ on g 2 G. n When V = F , so we have a homomorphism φ: G ! GLn(F ), we call it a matrix representation. matrix representation The choice of field F matters. For now, we will look exclusively at the case of F = C, i.e., representations in complex vector spaces. Remark. Since R ⊆ C is a subfield, GLn(R) is a subgroup of GLn(C). -
7.2 Binary Operators Closure
last edited April 19, 2016 7.2 Binary Operators A precise discussion of symmetry benefits from the development of what math- ematicians call a group, which is a special kind of set we have not yet explicitly considered. However, before we define a group and explore its properties, we reconsider several familiar sets and some of their most basic features. Over the last several sections, we have considered many di↵erent kinds of sets. We have considered sets of integers (natural numbers, even numbers, odd numbers), sets of rational numbers, sets of vertices, edges, colors, polyhedra and many others. In many of these examples – though certainly not in all of them – we are familiar with rules that tell us how to combine two elements to form another element. For example, if we are dealing with the natural numbers, we might considered the rules of addition, or the rules of multiplication, both of which tell us how to take two elements of N and combine them to give us a (possibly distinct) third element. This motivates the following definition. Definition 26. Given a set S,abinary operator ? is a rule that takes two elements a, b S and manipulates them to give us a third, not necessarily distinct, element2 a?b. Although the term binary operator might be new to us, we are already familiar with many examples. As hinted to earlier, the rule for adding two numbers to give us a third number is a binary operator on the set of integers, or on the set of rational numbers, or on the set of real numbers. -
3. Closed Sets, Closures, and Density
3. Closed sets, closures, and density 1 Motivation Up to this point, all we have done is define what topologies are, define a way of comparing two topologies, define a method for more easily specifying a topology (as a collection of sets generated by a basis), and investigated some simple properties of bases. At this point, we will start introducing some more interesting definitions and phenomena one might encounter in a topological space, starting with the notions of closed sets and closures. Thinking back to some of the motivational concepts from the first lecture, this section will start us on the road to exploring what it means for two sets to be \close" to one another, or what it means for a point to be \close" to a set. We will draw heavily on our intuition about n convergent sequences in R when discussing the basic definitions in this section, and so we begin by recalling that definition from calculus/analysis. 1 n Definition 1.1. A sequence fxngn=1 is said to converge to a point x 2 R if for every > 0 there is a number N 2 N such that xn 2 B(x) for all n > N. 1 Remark 1.2. It is common to refer to the portion of a sequence fxngn=1 after some index 1 N|that is, the sequence fxngn=N+1|as a tail of the sequence. In this language, one would phrase the above definition as \for every > 0 there is a tail of the sequence inside B(x)." n Given what we have established about the topological space Rusual and its standard basis of -balls, we can see that this is equivalent to saying that there is a tail of the sequence inside any open set containing x; this is because the collection of -balls forms a basis for the usual topology, and thus given any open set U containing x there is an such that x 2 B(x) ⊆ U. -
Class Numbers of Totally Real Number Fields
CLASS NUMBERS OF TOTALLY REAL NUMBER FIELDS BY JOHN C. MILLER A dissertation submitted to the Graduate School|New Brunswick Rutgers, The State University of New Jersey in partial fulfillment of the requirements for the degree of Doctor of Philosophy Graduate Program in Mathematics Written under the direction of Henryk Iwaniec and approved by New Brunswick, New Jersey May, 2015 ABSTRACT OF THE DISSERTATION Class numbers of totally real number fields by John C. Miller Dissertation Director: Henryk Iwaniec The determination of the class number of totally real fields of large discriminant is known to be a difficult problem. The Minkowski bound is too large to be useful, and the root discriminant of the field can be too large to be treated by Odlyzko's discriminant bounds. This thesis describes a new approach. By finding nontrivial lower bounds for sums over prime ideals of the Hilbert class field, we establish upper bounds for class numbers of fields of larger discriminant. This analytic upper bound, together with algebraic arguments concerning the divisibility properties of class numbers, allows us to determine the class numbers of many number fields that have previously been untreatable by any known method. For example, we consider the cyclotomic fields and their real subfields. Surprisingly, the class numbers of cyclotomic fields have only been determined for fields of small conductor, e.g. for prime conductors up to 67, due to the problem of finding the class number of its maximal real subfield, a problem first considered by Kummer. Our results have significantly improved the situation. We also study the cyclotomic Zp-extensions of the rationals. -
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. -
REPRESENTATION THEORY WEEK 7 1. Characters of GL Kand Sn A
REPRESENTATION THEORY WEEK 7 1. Characters of GLk and Sn A character of an irreducible representation of GLk is a polynomial function con- stant on every conjugacy class. Since the set of diagonalizable matrices is dense in GLk, a character is defined by its values on the subgroup of diagonal matrices in GLk. Thus, one can consider a character as a polynomial function of x1,...,xk. Moreover, a character is a symmetric polynomial of x1,...,xk as the matrices diag (x1,...,xk) and diag xs(1),...,xs(k) are conjugate for any s ∈ Sk. For example, the character of the standard representation in E is equal to x1 + ⊗n n ··· + xk and the character of E is equal to (x1 + ··· + xk) . Let λ = (λ1,...,λk) be such that λ1 ≥ λ2 ≥ ···≥ λk ≥ 0. Let Dλ denote the λj determinant of the k × k-matrix whose i, j entry equals xi . It is clear that Dλ is a skew-symmetric polynomial of x1,...,xk. If ρ = (k − 1,..., 1, 0) then Dρ = i≤j (xi − xj) is the well known Vandermonde determinant. Let Q Dλ+ρ Sλ = . Dρ It is easy to see that Sλ is a symmetric polynomial of x1,...,xk. It is called a Schur λ1 λk polynomial. The leading monomial of Sλ is the x ...xk (if one orders monomials lexicographically) and therefore it is not hard to show that Sλ form a basis in the ring of symmetric polynomials of x1,...,xk. Theorem 1.1. The character of Wλ equals to Sλ. I do not include a proof of this Theorem since it uses beautiful but hard combina- toric. -
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. -
Program of the Sessions Washington, District of Columbia, January 5–8, 2009
Program of the Sessions Washington, District of Columbia, January 5–8, 2009 MAA Short Course on Data Mining and New Saturday, January 3 Trends in Teaching Statistics (Part I) 8:00 AM –4:00PM Delaware Suite A, Lobby Level, Marriott Organizer: Richard D. De Veaux, Williams College AMS Short Course on Quantum Computation 8:00AM Registration and Quantum Information (Part I) 9:00AM Math is music—statistics is literature. (5) What are the challenges of teaching 8:00 AM –5:00PM Virginia Suite A, statistics, and why is it different from Lobby Level, Marriott mathematics? Richard D. De Veaux, Williams College Organizer: Samuel J. Lomonaco, 10:30AM Break. University of Maryland Baltimore County 10:45AM What does the introductory course look (6) like in 2009? How technology has 8:00AM Registration. changed what we do in introductory 9:00AM A Rosetta Stone for quantum computing. statistics for the non-math/science (1) Samuel Lomonaco,Universityof student. Maryland Baltimore County Richard D. De Veaux, Williams College 10:15AM Break. 1:00PM What does the math-based introductory (7) course look like in 2009? How do 10:45AM Quantum algorithms. we merge mathematical concepts (2) Peter Shor, Massachusetts Institute of into the introductory course for the Technology math/science student? How does 2:00PM Concentration of measure effects in statistical programming fit in? (3) quantum information. Richard D. De Veaux, Williams College Patrick Hayden, McGill University 2:30PM Break. 3:15PM Break. 2:45PM Introduction to Modeling. Regression and 3:45PM Quantum error correction and fault (8) ANOVA. Overview: How much to teach (4) tolerance. -
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]. -
Introduction to L-Functions: Dedekind Zeta Functions
Introduction to L-functions: Dedekind zeta functions Paul Voutier CIMPA-ICTP Research School, Nesin Mathematics Village June 2017 Dedekind zeta function Definition Let K be a number field. We define for Re(s) > 1 the Dedekind zeta function ζK (s) of K by the formula X −s ζK (s) = NK=Q(a) ; a where the sum is over all non-zero integral ideals, a, of OK . Euler product exists: Y −s −1 ζK (s) = 1 − NK=Q(p) ; p where the product extends over all prime ideals, p, of OK . Re(s) > 1 Proposition For any s = σ + it 2 C with σ > 1, ζK (s) converges absolutely. Proof: −n Y −s −1 Y 1 jζ (s)j = 1 − N (p) ≤ 1 − = ζ(σ)n; K K=Q pσ p p since there are at most n = [K : Q] many primes p lying above each rational prime p and NK=Q(p) ≥ p. A reminder of some algebraic number theory If [K : Q] = n, we have n embeddings of K into C. r1 embeddings into R and 2r2 embeddings into C, where n = r1 + 2r2. We will label these σ1; : : : ; σr1 ; σr1+1; σr1+1; : : : ; σr1+r2 ; σr1+r2 . If α1; : : : ; αn is a basis of OK , then 2 dK = (det (σi (αj ))) : Units in OK form a finitely-generated group of rank r = r1 + r2 − 1. Let u1;:::; ur be a set of generators. For any embedding σi , set Ni = 1 if it is real, and Ni = 2 if it is complex. Then RK = det (Ni log jσi (uj )j)1≤i;j≤r : wK is the number of roots of unity contained in K. -
Milnor's Problem on the Growth of Groups And
Milnor’s Problem on the Growth of Groups and its Consequences R. Grigorchuk REPORT No. 6, 2011/2012, spring ISSN 1103-467X ISRN IML-R- -6-11/12- -SE+spring MILNOR’S PROBLEM ON THE GROWTH OF GROUPS AND ITS CONSEQUENCES ROSTISLAV GRIGORCHUK Dedicated to John Milnor on the occasion of his 80th birthday. Abstract. We present a survey of results related to Milnor’s problem on group growth. We discuss the cases of polynomial growth and exponential but not uniformly exponential growth; the main part of the article is devoted to the intermediate (between polynomial and exponential) growth case. A number of related topics (growth of manifolds, amenability, asymptotic behavior of random walks) are considered, and a number of open problems are suggested. 1. Introduction The notion of the growth of a finitely generated group was introduced by A.S. Schwarz (also spelled Schvarts and Svarc))ˇ [218] and independently by Milnor [171, 170]. Particu- lar studies of group growth and their use in various situations have appeared in the works of Krause [149], Adelson-Velskii and Shreider [1], Dixmier [67], Dye [68, 69], Arnold and Krylov [7], Kirillov [142], Avez [8], Guivarc’h [127, 128, 129], Hartley, Margulis, Tempelman and other researchers. The note of Schwarz did not attract a lot of attention in the mathe- matical community, and was essentially unknown to mathematicians both in the USSR and the West (the same happened with papers of Adelson-Velskii, Dixmier and of some other mathematicians). By contrast, the note of Milnor [170], and especially the problem raised by him in [171], initiated a lot of activity and opened new directions in group theory and areas of its applications. -
SMALL ISOSPECTRAL and NONISOMETRIC ORBIFOLDS of DIMENSION 2 and 3 Introduction. in 1966
SMALL ISOSPECTRAL AND NONISOMETRIC ORBIFOLDS OF DIMENSION 2 AND 3 BENJAMIN LINOWITZ AND JOHN VOIGHT ABSTRACT. Revisiting a construction due to Vigneras,´ we exhibit small pairs of orbifolds and man- ifolds of dimension 2 and 3 arising from arithmetic Fuchsian and Kleinian groups that are Laplace isospectral (in fact, representation equivalent) but nonisometric. Introduction. In 1966, Kac [48] famously posed the question: “Can one hear the shape of a drum?” In other words, if you know the frequencies at which a drum vibrates, can you determine its shape? Since this question was asked, hundreds of articles have been written on this general topic, and it remains a subject of considerable interest [41]. Let (M; g) be a connected, compact Riemannian manifold (with or without boundary). Asso- ciated to M is the Laplace operator ∆, defined by ∆(f) = − div(grad(f)) for f 2 L2(M; g) a square-integrable function on M. The eigenvalues of ∆ on the space L2(M; g) form an infinite, discrete sequence of nonnegative real numbers 0 = λ0 < λ1 ≤ λ2 ≤ ::: , called the spectrum of M. In the case that M is a planar domain, the eigenvalues in the spectrum of M are essentially the frequencies produced by a drum shaped like M and fixed at its boundary. Two Riemannian manifolds are said to be Laplace isospectral if they have the same spectra. Inverse spectral geom- etry asks the extent to which the geometry and topology of M are determined by its spectrum. For example, volume, dimension and scalar curvature can all be shown to be spectral invariants.