Lecture 8 - the Extended Complex Plane Cˆ, Rational Functions, M¨Obius Transformations
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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. -
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. -
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. -
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 -
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. -
Complex Analysis
Complex Analysis Andrew Kobin Fall 2010 Contents Contents Contents 0 Introduction 1 1 The Complex Plane 2 1.1 A Formal View of Complex Numbers . .2 1.2 Properties of Complex Numbers . .4 1.3 Subsets of the Complex Plane . .5 2 Complex-Valued Functions 7 2.1 Functions and Limits . .7 2.2 Infinite Series . 10 2.3 Exponential and Logarithmic Functions . 11 2.4 Trigonometric Functions . 14 3 Calculus in the Complex Plane 16 3.1 Line Integrals . 16 3.2 Differentiability . 19 3.3 Power Series . 23 3.4 Cauchy's Theorem . 25 3.5 Cauchy's Integral Formula . 27 3.6 Analytic Functions . 30 3.7 Harmonic Functions . 33 3.8 The Maximum Principle . 36 4 Meromorphic Functions and Singularities 37 4.1 Laurent Series . 37 4.2 Isolated Singularities . 40 4.3 The Residue Theorem . 42 4.4 Some Fourier Analysis . 45 4.5 The Argument Principle . 46 5 Complex Mappings 47 5.1 M¨obiusTransformations . 47 5.2 Conformal Mappings . 47 5.3 The Riemann Mapping Theorem . 47 6 Riemann Surfaces 48 6.1 Holomorphic and Meromorphic Maps . 48 6.2 Covering Spaces . 52 7 Elliptic Functions 55 7.1 Elliptic Functions . 55 7.2 Elliptic Curves . 61 7.3 The Classical Jacobian . 67 7.4 Jacobians of Higher Genus Curves . 72 i 0 Introduction 0 Introduction These notes come from a semester course on complex analysis taught by Dr. Richard Carmichael at Wake Forest University during the fall of 2010. The main topics covered include Complex numbers and their properties Complex-valued functions Line integrals Derivatives and power series Cauchy's Integral Formula Singularities and the Residue Theorem The primary reference for the course and throughout these notes is Fisher's Complex Vari- ables, 2nd edition. -
Zeros of Differential Polynomials in Real Meromorphic Functions
Zeros of differential polynomials in real meromorphic functions Walter Bergweiler∗, Alex Eremenko† and Jim Langley June 30, 2004 Abstract We investigate when differential polynomials in real transcendental meromorphic functions have non-real zeros. For example, we show that if g is a real transcendental meromorphic function, c R 0 n ∈ \{ } and n 3 is an integer, then g0g c has infinitely many non-real zeros.≥ If g has only finitely many poles,− then this holds for n 2. Related results for rational functions g are also considered. ≥ 1 Introduction and results Our starting point is the following result due to Sheil-Small [15] which solved a longstanding conjecture. 2 Theorem A Let f be a real polynomial of degree d. Then f 0 + f has at least d 1 distinct non-real zeros which are not zeros of f . − In the special case that f has only real roots this theorem is due to Pr¨ufer [14, Ch. V, 182]; see [15] for further discussion of the result. The following Theorem B is an analogue of Theorem A for transcendental meromorphic functions. Here “meromorphic” will mean “meromorphic in the complex plane” unless explicitly stated otherwise. A meromorphic function is called real if it maps the real axis R to R . ∪{∞} ∗Supported by the German-Israeli Foundation for Scientific Research and Development (G.I.F.), grant no. G-643-117.6/1999 †Supported by NSF grants DMS-0100512 and DMS-0244421. 1 Theorem B Let f be a real transcendental meromorphic function with 2 finitely many poles. Then f 0 + f has infinitely many non-real zeros which are not zeros of f . -
Notes on Riemann's Zeta Function
NOTES ON RIEMANN’S ZETA FUNCTION DRAGAN MILICIˇ C´ 1. Gamma function 1.1. Definition of the Gamma function. The integral ∞ Γ(z)= tz−1e−tdt Z0 is well-defined and defines a holomorphic function in the right half-plane {z ∈ C | Re z > 0}. This function is Euler’s Gamma function. First, by integration by parts ∞ ∞ ∞ Γ(z +1)= tze−tdt = −tze−t + z tz−1e−t dt = zΓ(z) Z0 0 Z0 for any z in the right half-plane. In particular, for any positive integer n, we have Γ(n) = (n − 1)Γ(n − 1)=(n − 1)!Γ(1). On the other hand, ∞ ∞ Γ(1) = e−tdt = −e−t = 1; Z0 0 and we have the following result. 1.1.1. Lemma. Γ(n) = (n − 1)! for any n ∈ Z. Therefore, we can view the Gamma function as a extension of the factorial. 1.2. Meromorphic continuation. Now we want to show that Γ extends to a meromorphic function in C. We start with a technical lemma. Z ∞ 1.2.1. Lemma. Let cn, n ∈ +, be complex numbers such such that n=0 |cn| converges. Let P S = {−n | n ∈ Z+ and cn 6=0}. Then ∞ c f(z)= n z + n n=0 X converges absolutely for z ∈ C − S and uniformly on bounded subsets of C − S. The function f is a meromorphic function on C with simple poles at the points in S and Res(f, −n)= cn for any −n ∈ S. 1 2 D. MILICIˇ C´ Proof. Clearly, if |z| < R, we have |z + n| ≥ |n − R| for all n ≥ R. -
4. Complex Analysis, Rational and Meromorphic Asymptotics
ANALYTIC COMBINATORICS P A R T T W O 4. Complex Analysis, Rational and Meromorphic Asymptotics http://ac.cs.princeton.edu ANALYTIC COMBINATORICS P A R T T W O 4. Complex Analysis, Rational and Meromorphic functions Analytic Combinatorics •Roadmap Philippe Flajolet and •Complex functions Robert Sedgewick OF •Rational functions •Analytic functions and complex integration CAMBRIDGE •Meromorphic functions http://ac.cs.princeton.edu II.4a.CARM.Roadmap Analytic combinatorics overview specification A. SYMBOLIC METHOD 1. OGFs 2. EGFs GF equation 3. MGFs B. COMPLEX ASYMPTOTICS SYMBOLIC METHOD asymptotic ⬅ 4. Rational & Meromorphic estimate 5. Applications of R&M COMPLEX ASYMPTOTICS 6. Singularity Analysis desired 7. Applications of SA result ! 8. Saddle point 3 Starting point The symbolic method supplies generating functions that vary widely in nature. − + √ + + + ...+ − ()= ()= − ()= ()= ... − − − − − − ()= ()= ln + / ( )( ) ...( ) ()= − − − − − Next step: Derive asymptotic estimates of coefficients. − []() [ ]() [ ]() + [ ]()=β ∼ ∼ ∼ (ln ) / √ / / − − − [ ]() [ ]()=ln [ ]() ∼ ! ∼ √ Classical approach: Develop explicit expressions for coefficients, then approximate Analytic combinatorics approach: Direct approximations. 4 Starting point Catalan trees Derangements Construction G = ○ × SEQ( G ) Construction D = SET (CYC>1( Z )) ln ()= ()= − OGF equation () EGF equation − − + √ − Explicit form of OGF Explicit form of EGF = ()= − − ( ) Expansion ()= ( ) Expansion ()= − − − ! " # -
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. -
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. -
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.