Algebraic Function Fields

Total Page:16

File Type:pdf, Size:1020Kb

Algebraic Function Fields Math 612 { Algebraic Function Fields Julia Hartmann May 12, 2015 An algebraic function field is a finitely generated extension of transcendence degree 1 over some base field. This type of field extension occurs naturally in various branches of mathematics such as algebraic geometry, number theory, and the theory of compact Riemann surfaces. In this course, we study algebraic function fields from an algebraic point of view. Topics include: • Valuations on a function field: valuation rings, places, rational function fields, weak approximation • Divisors of a function field: divisor group, dimension of a divisor • Genus of a function field: definition, adeles, index of speciality • Riemann-Roch theorem: Weil differentials, divisor of a differential, duality theorem and Riemann-Roch theorem, canonical class, strong approxima- tion • Fields of genus 0: divisor classes, rational function fields • Extensions of valuations: ramification index and relative degree, funda- mental equation, extensions and automorphisms • Completions: definition and existence of completions, complete discretely valued case, Hensel-Lemma, completions of function fields • The different: different exponent, Dedekind's different theorem • Hurwitz genus formula: divisors in extensions, lifting of differentials, genus formula, L¨uroth'stheorem, constant extensions, inseparable extensions • Differentials of algebraic function fields: derviations, differentials, residues • The residue theorem: connection between differentials and Weil differen- tials, residue theorem • Congruence zeta function: finiteness theorems, zeta function, smallest positive divisor degree, functional equation, Riemann hypothesis for con- gruence function fields (Hasse-Weil) 1 • Applications to coding theory: gemetric Goppa codes, rational codes, de- coding, automorphisms, asymptotic bounds • Elliptic function fields and elliptic curves Additional topics will be added as time permits and based on students' interests. Prerequisites for the class are a solid background in algebra, in particular, field extensions and Galois theory. No prior knowledge of valuation theory will be assumed. This course is also suitable for undergraduate or master students who have taken 502/503. Grading will be based on regular homework assignments and a presentation or oral exam at the end of the semester. 2.
Recommended publications
  • Partial Fractions Decompositions (Includes the Coverup Method)
    Partial Fractions and the Coverup Method 18.031 Haynes Miller and Jeremy Orloff *Much of this note is freely borrowed from an MIT 18.01 note written by Arthur Mattuck. 1 Heaviside Cover-up Method 1.1 Introduction The cover-up method was introduced by Oliver Heaviside as a fast way to do a decomposition into partial fractions. This is an essential step in using the Laplace transform to solve differential equations, and this was more or less Heaviside's original motivation. The cover-up method can be used to make a partial fractions decomposition of a proper p(s) rational function whenever the denominator can be factored into distinct linear factors. q(s) Note: We put this section first, because the coverup method is so useful and many people have not seen it. Some of the later examples rely on the full algebraic method of undeter- mined coefficients presented in the next section. If you have never seen partial fractions you should read that section first. 1.2 Linear Factors We first show how the method works on a simple example, and then show why it works. s − 7 Example 1. Decompose into partial fractions. (s − 1)(s + 2) answer: We know the answer will have the form s − 7 A B = + : (1) (s − 1)(s + 2) s − 1 s + 2 To determine A by the cover-up method, on the left-hand side we mentally remove (or cover up with a finger) the factor s − 1 associated with A, and substitute s = 1 into what's left; this gives A: s − 7 1 − 7 = = −2 = A: (2) (s + 2) s=1 1 + 2 Similarly, B is found by covering up the factor s + 2 on the left, and substituting s = −2 into what's left.
    [Show full text]
  • Field Theory Pete L. Clark
    Field Theory Pete L. Clark Thanks to Asvin Gothandaraman and David Krumm for pointing out errors in these notes. Contents About These Notes 7 Some Conventions 9 Chapter 1. Introduction to Fields 11 Chapter 2. Some Examples of Fields 13 1. Examples From Undergraduate Mathematics 13 2. Fields of Fractions 14 3. Fields of Functions 17 4. Completion 18 Chapter 3. Field Extensions 23 1. Introduction 23 2. Some Impossible Constructions 26 3. Subfields of Algebraic Numbers 27 4. Distinguished Classes 29 Chapter 4. Normal Extensions 31 1. Algebraically closed fields 31 2. Existence of algebraic closures 32 3. The Magic Mapping Theorem 35 4. Conjugates 36 5. Splitting Fields 37 6. Normal Extensions 37 7. The Extension Theorem 40 8. Isaacs' Theorem 40 Chapter 5. Separable Algebraic Extensions 41 1. Separable Polynomials 41 2. Separable Algebraic Field Extensions 44 3. Purely Inseparable Extensions 46 4. Structural Results on Algebraic Extensions 47 Chapter 6. Norms, Traces and Discriminants 51 1. Dedekind's Lemma on Linear Independence of Characters 51 2. The Characteristic Polynomial, the Trace and the Norm 51 3. The Trace Form and the Discriminant 54 Chapter 7. The Primitive Element Theorem 57 1. The Alon-Tarsi Lemma 57 2. The Primitive Element Theorem and its Corollary 57 3 4 CONTENTS Chapter 8. Galois Extensions 61 1. Introduction 61 2. Finite Galois Extensions 63 3. An Abstract Galois Correspondence 65 4. The Finite Galois Correspondence 68 5. The Normal Basis Theorem 70 6. Hilbert's Theorem 90 72 7. Infinite Algebraic Galois Theory 74 8. A Characterization of Normal Extensions 75 Chapter 9.
    [Show full text]
  • Z-Transform Part 2 February 23, 2017 1 / 38 the Z-Transform and Its Application to the Analysis of LTI Systems
    ELC 4351: Digital Signal Processing Liang Dong Electrical and Computer Engineering Baylor University liang [email protected] February 23, 2017 Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 1 / 38 The z-Transform and Its Application to the Analysis of LTI Systems 1 Rational z-Transform 2 Inversion of the z-Transform 3 Analysis of LTI Systems in the z-Domain 4 Causality and Stability Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 2 / 38 Rational z-Transforms X (z) is a rational function, that is, a ratio of two polynomials in z−1 (or z). B(z) X (z) = A(z) −1 −M b0 + b1z + ··· + bM z = −1 −N a0 + a1z + ··· aN z PM b z−k = k=0 k PN −k k=0 ak z Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 3 / 38 Rational z-Transforms X (z) is a rational function, that is, a ratio of two polynomials B(z) and A(z). The polynomials can be expressed in factored forms. B(z) X (z) = A(z) b (z − z )(z − z ) ··· (z − z ) = 0 z−M+N 1 2 M a0 (z − p1)(z − p2) ··· (z − pN ) b QM (z − z ) = 0 zN−M k=1 k a QN 0 k=1(z − pk ) Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 4 / 38 Poles and Zeros The zeros of a z-transform X (z) are the values of z for which X (z) = 0. The poles of a z-transform X (z) are the values of z for which X (z) = 1.
    [Show full text]
  • Section 1.2 – Mathematical Models: a Catalog of Essential Functions
    Section 1-2 © Sandra Nite Math 131 Lecture Notes Section 1.2 – Mathematical Models: A Catalog of Essential Functions A mathematical model is a mathematical description of a real-world situation. Often the model is a function rule or equation of some type. Modeling Process Real-world Formulate Test/Check problem Real-world Mathematical predictions model Interpret Mathematical Solve conclusions Linear Models Characteristics: • The graph is a line. • In the form y = f(x) = mx + b, m is the slope of the line, and b is the y-intercept. • The rate of change (slope) is constant. • When the independent variable ( x) in a table of values is sequential (same differences), the dependent variable has successive differences that are the same. • The linear parent function is f(x) = x, with D = ℜ = (-∞, ∞) and R = ℜ = (-∞, ∞). • The direct variation function is a linear function with b = 0 (goes through the origin). • In a direct variation function, it can be said that f(x) varies directly with x, or f(x) is directly proportional to x. Example: See pp. 26-28 of the text. 1 Section 1-2 © Sandra Nite Polynomial Functions = n + n−1 +⋅⋅⋅+ 2 + + A function P is called a polynomial if P(x) an x an−1 x a2 x a1 x a0 where n is a nonnegative integer and a0 , a1 , a2 ,..., an are constants called the coefficients of the polynomial. If the leading coefficient an ≠ 0, then the degree of the polynomial is n. Characteristics: • The domain is the set of all real numbers D = (-∞, ∞). • If the degree is odd, the range R = (-∞, ∞).
    [Show full text]
  • Introduction to Finite Fields, I
    Spring 2010 Chris Christensen MAT/CSC 483 Introduction to finite fields, I Fields and rings To understand IDEA, AES, and some other modern cryptosystems, it is necessary to understand a bit about finite fields. A field is an algebraic object. The elements of a field can be added and subtracted and multiplied and divided (except by 0). Often in undergraduate mathematics courses (e.g., calculus and linear algebra) the numbers that are used come from a field. The rational a numbers = :ab , are integers and b≠ 0 form a field; rational numbers (i.e., fractions) b can be added (and subtracted) and multiplied (and divided). The real numbers form a field. The complex numbers also form a field. Number theory studies the integers . The integers do not form a field. Integers can be added and subtracted and multiplied, but integers cannot always be divided. Sure, 6 5 divided by 3 is 2; but 5 divided by 2 is not an integer; is a rational number. The 2 integers form a ring, but the rational numbers form a field. Similarly the polynomials with integer coefficients form a ring. We can add and subtract polynomials with integer coefficients, and the result will be a polynomial with integer coefficients. We can multiply polynomials with integer coefficients, and the result will be a polynomial with integer coefficients. But, we cannot always divide polynomials X 2 − 4 XX3 +−2 with integer coefficients: =X + 2 , but is not a polynomial – it is a X − 2 X 2 + 7 rational function. The polynomials with integer coefficients do not form a field, they form a ring.
    [Show full text]
  • Calculus Terminology
    AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential
    [Show full text]
  • Arxiv:1712.01752V2 [Cs.SC] 25 Oct 2018 Figure 1
    Symbolic-Numeric Integration of Rational Functions Robert M Corless1, Robert HC Moir1, Marc Moreno Maza1, Ning Xie2 1Ontario Research Center for Computer Algebra, University of Western Ontario, Canada 2Huawei Technologies Corporation, Markham, ON Abstract. We consider the problem of symbolic-numeric integration of symbolic functions, focusing on rational functions. Using a hybrid method allows the stable yet efficient computation of symbolic antideriva- tives while avoiding issues of ill-conditioning to which numerical methods are susceptible. We propose two alternative methods for exact input that compute the rational part of the integral using Hermite reduction and then compute the transcendental part two different ways using a combi- nation of exact integration and efficient numerical computation of roots. The symbolic computation is done within bpas, or Basic Polynomial Al- gebra Subprograms, which is a highly optimized environment for poly- nomial computation on parallel architectures, while the numerical com- putation is done using the highly optimized multiprecision rootfinding package MPSolve. We show that both methods are forward and back- ward stable in a structured sense and away from singularities tolerance proportionality is achieved by adjusting the precision of the rootfinding tasks. 1 Introduction Hybrid symbolic-numeric integration of rational functions is interesting for sev- eral reasons. First, a formula, not a number or a computer program or subroutine, may be desired, perhaps for further analysis such as by taking asymptotics. In this case one typically wants an exact symbolic answer, and for rational func- tions this is in principle always possible. However, an exact symbolic answer may be too cluttered with algebraic numbers or lengthy rational numbers to be intelligible or easily analyzed by further symbolic manipulation.
    [Show full text]
  • Lecture 8 - the Extended Complex Plane Cˆ, Rational Functions, M¨Obius Transformations
    Math 207 - Spring '17 - Fran¸coisMonard 1 Lecture 8 - The extended complex plane C^, rational functions, M¨obius transformations Material: [G]. [SS, Ch.3 Sec. 3] 1 The purpose of this lecture is to \compactify" C by adjoining to it a point at infinity , and to extend to concept of analyticity there. Let us first define: a neighborhood of infinity U is the complement of a closed, bounded set. A \basis of neighborhoods" is given by complements of closed disks of the form Uz0,ρ = C − Dρ(z0) = fjz − z0j > ρg; z0 2 C; ρ > 0: Definition 1. For U a nbhd of 1, the function f : U ! C has a limit at infinity iff there exists L 2 C such that for every " > 0, there exists R > 0 such that for any jzj > R, we have jf(z)−Lj < ". 1 We write limz!1 f(z) = L. Equivalently, limz!1 f(z) = L if and only if limz!0 f z = L. With this concept, the algebraic limit rules hold in the same way that they hold at finite points when limits are finite. 1 Example 1. • limz!1 z = 0. z2+1 1 • limz!1 (z−1)(3z+7) = 3 . z 1 • limz!1 e does not exist (this is because e z has an essential singularity at z = 0). A way 1 0 1 to prove this is that both sequences zn = 2nπi and zn = 2πi(n+1=2) converge to zero, while the 1 1 0 sequences e zn and e zn converge to different limits, 1 and 0 respectively.
    [Show full text]
  • Chapter 2 Complex Analysis
    Chapter 2 Complex Analysis In this part of the course we will study some basic complex analysis. This is an extremely useful and beautiful part of mathematics and forms the basis of many techniques employed in many branches of mathematics and physics. We will extend the notions of derivatives and integrals, familiar from calculus, to the case of complex functions of a complex variable. In so doing we will come across analytic functions, which form the centerpiece of this part of the course. In fact, to a large extent complex analysis is the study of analytic functions. After a brief review of complex numbers as points in the complex plane, we will ¯rst discuss analyticity and give plenty of examples of analytic functions. We will then discuss complex integration, culminating with the generalised Cauchy Integral Formula, and some of its applications. We then go on to discuss the power series representations of analytic functions and the residue calculus, which will allow us to compute many real integrals and in¯nite sums very easily via complex integration. 2.1 Analytic functions In this section we will study complex functions of a complex variable. We will see that di®erentiability of such a function is a non-trivial property, giving rise to the concept of an analytic function. We will then study many examples of analytic functions. In fact, the construction of analytic functions will form a basic leitmotif for this part of the course. 2.1.1 The complex plane We already discussed complex numbers briefly in Section 1.3.5.
    [Show full text]
  • CYCLIC RESULTANTS 1. Introduction the M-Th Cyclic Resultant of A
    CYCLIC RESULTANTS CHRISTOPHER J. HILLAR Abstract. We characterize polynomials having the same set of nonzero cyclic resultants. Generically, for a polynomial f of degree d, there are exactly 2d−1 distinct degree d polynomials with the same set of cyclic resultants as f. How- ever, in the generic monic case, degree d polynomials are uniquely determined by their cyclic resultants. Moreover, two reciprocal (\palindromic") polyno- mials giving rise to the same set of nonzero cyclic resultants are equal. In the process, we also prove a unique factorization result in semigroup algebras involving products of binomials. Finally, we discuss how our results yield algo- rithms for explicit reconstruction of polynomials from their cyclic resultants. 1. Introduction The m-th cyclic resultant of a univariate polynomial f 2 C[x] is m rm = Res(f; x − 1): We are primarily interested here in the fibers of the map r : C[x] ! CN given by 1 f 7! (rm)m=0. In particular, what are the conditions for two polynomials to give rise to the same set of cyclic resultants? For technical reasons, we will only consider polynomials f that do not have a root of unity as a zero. With this restriction, a polynomial will map to a set of all nonzero cyclic resultants. Our main result gives a complete answer to this question. Theorem 1.1. Let f and g be polynomials in C[x]. Then, f and g generate the same sequence of nonzero cyclic resultants if and only if there exist u; v 2 C[x] with u(0) 6= 0 and nonnegative integers l1; l2 such that deg(u) ≡ l2 − l1 (mod 2), and f(x) = (−1)l2−l1 xl1 v(x)u(x−1)xdeg(u) g(x) = xl2 v(x)u(x): Remark 1.2.
    [Show full text]
  • Trigonometric Functions
    72 Chapter 4 Trigonometric Functions To define the radian measurement system, we consider the unit circle in the xy-plane: ........................ ....... ....... ...... ....................... .............. ............... ......... ......... ....... ....... ....... ...... ...... ...... ..... ..... ..... ..... ..... ..... .... ..... ..... .... .... .... .... ... (cos x, sin x) ... ... 4 ... A ..... .. ... ....... ... ... ....... ... .. ....... .. .. ....... .. .. ....... .. .. ....... .. .. ....... .. .. ....... ...... ....... ....... ...... ....... x . ....... Trigonometric Functions . ...... ....y . ....... (1, 0) . ....... ....... .. ...... .. .. ....... .. .. ....... .. .. ....... .. .. ....... .. ... ...... ... ... ....... ... ... .......... ... ... ... ... .... B... .... .... ..... ..... ..... ..... ..... ..... ..... ..... ...... ...... ...... ...... ....... ....... ........ ........ .......... .......... ................................................................................... An angle, x, at the center of the circle is associated with an arc of the circle which is said to subtend the angle. In the figure, this arc is the portion of the circle from point (1, 0) So far we have used only algebraic functions as examples when finding derivatives, that is, to point A. The length of this arc is the radian measure of the angle x; the fact that the functions that can be built up by the usual algebraic operations of addition, subtraction, radian measure is an actual geometric length is largely responsible for the usefulness of
    [Show full text]
  • Rational Functions
    Chapter 4 Rational Functions 4.1 Introduction to Rational Functions If we add, subtract or multiply polynomial functions according to the function arithmetic rules defined in Section 1.5, we will produce another polynomial function. If, on the other hand, we divide two polynomial functions, the result may not be a polynomial. In this chapter we study rational functions - functions which are ratios of polynomials. Definition 4.1. A rational function is a function which is the ratio of polynomial functions. Said differently, r is a rational function if it is of the form p(x) r(x) = ; q(x) where p and q are polynomial functions.a aAccording to this definition, all polynomial functions are also rational functions. (Take q(x) = 1). As we recall from Section 1.4, we have domain issues anytime the denominator of a fraction is zero. In the example below, we review this concept as well as some of the arithmetic of rational expressions. p(x) Example 4.1.1. Find the domain of the following rational functions. Write them in the form q(x) for polynomial functions p and q and simplify. 2x − 1 3 1. f(x) = 2. g(x) = 2 − x + 1 x + 1 2x2 − 1 3x − 2 2x2 − 1 3x − 2 3. h(x) = − 4. r(x) = ÷ x2 − 1 x2 − 1 x2 − 1 x2 − 1 Solution. 1. To find the domain of f, we proceed as we did in Section 1.4: we find the zeros of the denominator and exclude them from the domain. Setting x + 1 = 0 results in x = −1.
    [Show full text]