Complex Analysis Lecture Notes

Total Page:16

File Type:pdf, Size:1020Kb

Complex Analysis Lecture Notes Complex Analysis Lecture Notes Dan Romik About this document. These notes were created for use as primary reading material for the graduate course Math 205A: Complex Analysis at UC Davis. The current 2020 revision (dated June 15, 2021) updates my earlier version of the notes from 2018. With some exceptions, the exposition follows the textbook Complex Analysis by E. M. Stein and R. Shakarchi (Princeton Uni- versity Press, 2003). The notes are typeset in the Bera Serif font. Acknowledgements. I am grateful to Christopher Alexander, Jennifer Brown, Brynn Caddel, Keith Conrad, Bo Long, Anthony Nguyen, Jianping Pan, and Brad Velasquez for comments that helped me improve the notes. Figure 5 on page 27 was created by Jennifer Brown and is used with her permission. An anonymous contributor added an index and suggested the Bera Serif font and a few other improvements to the document design. You too can help me continue to improve these notes by emailing me at [email protected] with any comments or corrections you have. Complex Analysis Lecture Notes Document version: June 15, 2021 Copyright © 2020 by Dan Romik Cover figure: a heat map plot of the entire function z 7! z(z − 1)π−z=2Γ(z=2)ζ(z). Created with Mathematica using code by Simon Woods, available at http://mathematica.stackexchange.com/questions/7275/how-can-i-generate-this-domain-coloring-plot Contents 1 Introduction: why study complex analysis? 1 2 The fundamental theorem of algebra 3 3 Analyticity 7 4 Power series 13 5 Contour integrals 16 6 Cauchy’s theorem 21 7 Consequences of Cauchy’s theorem 26 8 Zeros, poles, and the residue theorem 35 9 Meromorphic functions and the Riemann sphere 38 10The argument principle 41 11Applications of Rouché’s theorem 45 12Simply-connected regions and Cauchy’s theorem 46 13The logarithm function 50 14The Euler gamma function 52 15The Riemann zeta function 59 16The prime number theorem 71 17Introduction to asymptotic analysis 79 Problems 92 Suggested topics for course projects 119 References 121 Index 122 1 1 INTRODUCTION: WHY STUDY COMPLEX ANALYSIS? 1 Introduction: why study complex analysis? These notes are about complex analysis, the area of mathematics that studies analytic functions of a complex variable and their properties. While this may sound a bit specialized, there are (at least) two excellent reasons why all mathematicians should learn about complex analysis. First, it is, in my humble opinion, one of the most beautiful areas of mathematics. One way of putting it that has occurred to me is that complex analysis has a very high ratio of theorems to definitions (i.e., a very low “entropy”): you get a lot more as “output” than you put in as “input.” The second reason is complex analysis has a large number of applications (in both the pure math and applied math senses of the word) to things that seem like they ought to have little to do with complex numbers. For example: • Solving polynomial equations: historically, this was the motivation for introducing complex numbers by Cardano, who published the famous formula for solving cubic equations in 1543, after learning of the solu- tion found earlier by Scipione del Ferro. An important point to keep in mind is that Cardano’s formula sometimes requires taking operations in the complex plane as an intermediate step to get to the final answer, even when the cubic equation being solved has only real roots. Example 1. Using Cardano’s formula, it can be found that the solutions to the cubic equation z3 + 6z2 + 9z + 3 = 0 are z1 = 2 cos(2π=9) − 2; z2 = 2 cos(8π=9) − 2; z3 = 2 sin(π=18) − 2: p n • Proving Stirling’s formula: n! ∼ 2πn(n=e) . Here, an ∼ bn is the stan- dard “asymptotic to” relation, defined to mean limn!1 an=bn = 1. n • Proving the prime number theorem: π(n) ∼ log n , where π(n) denotes the number of primes less than or equal to n (the prime-counting func- tion). 2 1 INTRODUCTION: WHY STUDY COMPLEX ANALYSIS? • Proving many other asymptotic formulas in number theory and combi- natorics, e.g. (to name one other of my favorite examples), the Hardy- Ramanujan formula 1 p p(n) ∼ p eπ 2n=3; 4 3n where p(n) is the number of integer partitions of n. • Evaluation of complicated definite integrals, for example Z 1 1rπ sin(t2) dt = : 0 2 2 (This application is strongly emphasized in older textbooks, and has been known to result in a mild case of post-traumatic stress disorder.) • Solving physics problems in hydrodynamics, heat conduction, electro- statics and more. • Analyzing alternating current electrical networks by extending Ohm’s law to electrical impedance. Complex analysis also has many other important applications in electrical engineering, signals processing and control theory. • Probability and combinatorics, e.g., the Cardy-Smirnov formula in per- colation theory and the connective constant for self-avoiding walks on the hexagonal lattice. • It was proved in 2016 that the optimal densities for sphere packing in 8 and 24 dimensions are π4=384 and π12=12!, respectively. The proofs make spectacular use of complex analysis (and more specifically, a part of complex analysis that studies certain special functions known as modular forms). • Nature uses complex numbers in Schrödinger’s equation and quantum field theory. This is not a mere mathematical convenience or sleight-of- hand, but in fact appears to be a built-in feature of the very equations describing our physical universe. Why? No one knows. • Conformal maps, which come up in purely geometric applications where the algebraic or analytic structure of complex numbers seems irrele- vant, are in fact deeply tied to complex analysis. Conformal maps were used by the Dutch artist M.C. Escher (though he had no mathematical training) to create amazing art, and used by others to better under- stand, and even to improve on, Escher’s work. See Fig. 1, and see [10] for more on the connection of Escher’s work to mathematics. 3 2 THE FUNDAMENTAL THEOREM OF ALGEBRA Figure 1: Print Gallery, a lithograph by M.C. Escher which was discovered to be based on a mathematical structure related to a complex function z 7! zα for a certain complex number α, although it was constructed by Escher purely using geometric intuition. See the paper [8] and this website, which has animated versions of Escher’s lithograph brought to life using the math- ematics of complex analysis. • Complex dynamics, e.g., the iconic Mandelbrot set. See Fig. 2. There are many other applications and beautiful connections of complex analysis to other areas of mathematics. (If you run across some interesting ones, please let me know!) In the next section I will begin our journey into the subject by illustrating a few beautiful ideas and along the way begin to review the concepts from undergraduate complex analysis. 2 The fundamental theorem of algebra One of the most famous theorems in complex analysis is the not-very-aptly named Fundamental Theorem of Algebra. This seems like a fitting place to start our journey into the theory. Theorem 1 (The Fundamental Theorem of Algebra.). Every nonconstant polynomial p(z) over the complex numbers has a root. 4 2 THE FUNDAMENTAL THEOREM OF ALGEBRA Figure 2: The Mandelbrot set. [Source: Wikipedia] The fundamental theorem of algebra is a subtle result that has many beautiful proofs. I will show you three of them. Let me know if you see any “algebra”. First proof: analytic proof. Let n n−1 p(z) = anz + an−1z + ::: + a0 be a polynomial of degree n ≥ 1, and consider where jp(z)j attains its infi- mum. First, note that it can’t happen as jzj ! 1, since n −1 −2 −n jp(z)j = jzj · (jan + an−1z + an−2z + ::: + a0z j); jp(z)j and in particular limjzj!1 jzjn = janj, so for large jzj it is guaranteed that jp(z)j ≥ jp(0)j = ja0j. Fixing some radius R > 0 for which jzj > R implies jp(z)j ≥ ja0j, we therefore have that m0 := inf jp(z)j = inf jp(z)j = min jp(z)j = jp(z0)j z2C jz|≤R jz|≤R where z0 = arg min jp(z)j, and the minimum exists because p(z) is a continu- jz|≤R ous function on the disc DR(0). Denote w0 = p(z0), so that m0 = jw0j. We now claim that m0 = 0. As- sume by contradiction that it doesn’t, and examine the local behavior of p(z) around z0; more precisely, expanding p(z) in powers of z − z0 we can write n X j k n p(z) = w0 + cj(z − z0) = w0 + ck(z − z0) + ::: + cn(z − z0) ; j=1 5 2 THE FUNDAMENTAL THEOREM OF ALGEBRA where k is the minimal positive index for which cj 6= 0. (Exercise: why can we expand p(z) in this way?) Now imagine starting with z = z0 and iθ traveling away from z0 in some direction e . What happens to p(z)? Well, the expansion gives iθ k ikθ k+1 i(k+1)θ n inθ p(z0 + re ) = w0 + ckr e + ck+1r e + ::: + cnr e : When r is very small, the power rk dominates the other terms rj with k < j ≤ n, i.e., iθ k ikθ i(k+1)θ n−k inθ p(z0 + re ) = w0 + r (cke + ck+1re + ::: + cnr e ) k ikθ = w0 + ckr e (1 + g(r; θ)); where limr!0 jg(r; θ)j = 0. To reach a contradiction, it is now enough to k ikθ choose θ so that the vector ckr e “points in the opposite direction” from w0, that is, such that c rkeikθ k 2 (−∞; 0): w0 1 Obviously this is possible: take θ = k (arg w0 − arg(ck) + π).
Recommended publications
  • Avoiding the Undefined by Underspecification
    Avoiding the Undefined by Underspecification David Gries* and Fred B. Schneider** Computer Science Department, Cornell University Ithaca, New York 14853 USA Abstract. We use the appeal of simplicity and an aversion to com- plexity in selecting a method for handling partial functions in logic. We conclude that avoiding the undefined by using underspecification is the preferred choice. 1 Introduction Everything should be made as simple as possible, but not any simpler. --Albert Einstein The first volume of Springer-Verlag's Lecture Notes in Computer Science ap- peared in 1973, almost 22 years ago. The field has learned a lot about computing since then, and in doing so it has leaned on and supported advances in other fields. In this paper, we discuss an aspect of one of these fields that has been explored by computer scientists, handling undefined terms in formal logic. Logicians are concerned mainly with studying logic --not using it. Some computer scientists have discovered, by necessity, that logic is actually a useful tool. Their attempts to use logic have led to new insights --new axiomatizations, new proof formats, new meta-logical notions like relative completeness, and new concerns. We, the authors, are computer scientists whose interests have forced us to become users of formal logic. We use it in our work on language definition and in the formal development of programs. This paper is born of that experience. The operation of a computer is nothing more than uninterpreted symbol ma- nipulation. Bits are moved and altered electronically, without regard for what they denote. It iswe who provide the interpretation, saying that one bit string represents money and another someone's name, or that one transformation rep- resents addition and another alphabetization.
    [Show full text]
  • Informal Lecture Notes for Complex Analysis
    Informal lecture notes for complex analysis Robert Neel I'll assume you're familiar with the review of complex numbers and their algebra as contained in Appendix G of Stewart's book, so we'll pick up where that leaves off. 1 Elementary complex functions In one-variable real calculus, we have a collection of basic functions, like poly- nomials, rational functions, the exponential and log functions, and the trig functions, which we understand well and which serve as the building blocks for more general functions. The same is true in one complex variable; in fact, the real functions we just listed can be extended to complex functions. 1.1 Polynomials and rational functions We start with polynomials and rational functions. We know how to multiply and add complex numbers, and thus we understand polynomial functions. To be specific, a degree n polynomial, for some non-negative integer n, is a function of the form n n−1 f(z) = cnz + cn−1z + ··· + c1z + c0; 3 where the ci are complex numbers with cn 6= 0. For example, f(z) = 2z + (1 − i)z + 2i is a degree three (complex) polynomial. Polynomials are clearly defined on all of C. A rational function is the quotient of two polynomials, and it is defined everywhere where the denominator is non-zero. z2+1 Example: The function f(z) = z2−1 is a rational function. The denomina- tor will be zero precisely when z2 = 1. We know that every non-zero complex number has n distinct nth roots, and thus there will be two points at which the denominator is zero.
    [Show full text]
  • MATH 305 Complex Analysis, Spring 2016 Using Residues to Evaluate Improper Integrals Worksheet for Sections 78 and 79
    MATH 305 Complex Analysis, Spring 2016 Using Residues to Evaluate Improper Integrals Worksheet for Sections 78 and 79 One of the interesting applications of Cauchy's Residue Theorem is to find exact values of real improper integrals. The idea is to integrate a complex rational function around a closed contour C that can be arbitrarily large. As the size of the contour becomes infinite, the piece in the complex plane (typically an arc of a circle) contributes 0 to the integral, while the part remaining covers the entire real axis (e.g., an improper integral from −∞ to 1). An Example Let us use residues to derive the formula p Z 1 x2 2 π 4 dx = : (1) 0 x + 1 4 Note the somewhat surprising appearance of π for the value of this integral. z2 First, let f(z) = and let C = L + C be the contour that consists of the line segment L z4 + 1 R R R on the real axis from −R to R, followed by the semi-circle CR of radius R traversed CCW (see figure below). Note that C is a positively oriented, simple, closed contour. We will assume that R > 1. Next, notice that f(z) has two singular points (simple poles) inside C. Call them z0 and z1, as shown in the figure. By Cauchy's Residue Theorem. we have I f(z) dz = 2πi Res f(z) + Res f(z) C z=z0 z=z1 On the other hand, we can parametrize the line segment LR by z = x; −R ≤ x ≤ R, so that I Z R x2 Z z2 f(z) dz = 4 dx + 4 dz; C −R x + 1 CR z + 1 since C = LR + CR.
    [Show full text]
  • A Quick Algebra Review
    A Quick Algebra Review 1. Simplifying Expressions 2. Solving Equations 3. Problem Solving 4. Inequalities 5. Absolute Values 6. Linear Equations 7. Systems of Equations 8. Laws of Exponents 9. Quadratics 10. Rationals 11. Radicals Simplifying Expressions An expression is a mathematical “phrase.” Expressions contain numbers and variables, but not an equal sign. An equation has an “equal” sign. For example: Expression: Equation: 5 + 3 5 + 3 = 8 x + 3 x + 3 = 8 (x + 4)(x – 2) (x + 4)(x – 2) = 10 x² + 5x + 6 x² + 5x + 6 = 0 x – 8 x – 8 > 3 When we simplify an expression, we work until there are as few terms as possible. This process makes the expression easier to use, (that’s why it’s called “simplify”). The first thing we want to do when simplifying an expression is to combine like terms. For example: There are many terms to look at! Let’s start with x². There Simplify: are no other terms with x² in them, so we move on. 10x x² + 10x – 6 – 5x + 4 and 5x are like terms, so we add their coefficients = x² + 5x – 6 + 4 together. 10 + (-5) = 5, so we write 5x. -6 and 4 are also = x² + 5x – 2 like terms, so we can combine them to get -2. Isn’t the simplified expression much nicer? Now you try: x² + 5x + 3x² + x³ - 5 + 3 [You should get x³ + 4x² + 5x – 2] Order of Operations PEMDAS – Please Excuse My Dear Aunt Sally, remember that from Algebra class? It tells the order in which we can complete operations when solving an equation.
    [Show full text]
  • Arxiv:1207.1472V2 [Math.CV]
    SOME SIMPLIFICATIONS IN THE PRESENTATIONS OF COMPLEX POWER SERIES AND UNORDERED SUMS OSWALDO RIO BRANCO DE OLIVEIRA Abstract. This text provides very easy and short proofs of some basic prop- erties of complex power series (addition, subtraction, multiplication, division, rearrangement, composition, differentiation, uniqueness, Taylor’s series, Prin- ciple of Identity, Principle of Isolated Zeros, and Binomial Series). This is done by simplifying the usual presentation of unordered sums of a (countable) family of complex numbers. All the proofs avoid formal power series, double series, iterated series, partial series, asymptotic arguments, complex integra- tion theory, and uniform continuity. The use of function continuity as well as epsilons and deltas is kept to a mininum. Mathematics Subject Classification: 30B10, 40B05, 40C15, 40-01, 97I30, 97I80 Key words and phrases: Power Series, Multiple Sequences, Series, Summability, Complex Analysis, Functions of a Complex Variable. Contents 1. Introduction 1 2. Preliminaries 2 3. Absolutely Convergent Series and Commutativity 3 4. Unordered Countable Sums and Commutativity 5 5. Unordered Countable Sums and Associativity. 9 6. Sum of a Double Sequence and The Cauchy Product 10 7. Power Series - Algebraic Properties 11 8. Power Series - Analytic Properties 14 References 17 arXiv:1207.1472v2 [math.CV] 27 Jul 2012 1. Introduction The objective of this work is to provide a simplification of the theory of un- ordered sums of a family of complex numbers (in particular, for a countable family of complex numbers) as well as very easy proofs of basic operations and properties concerning complex power series, such as addition, scalar multiplication, multipli- cation, division, rearrangement, composition, differentiation (see Apostol [2] and Vyborny [21]), Taylor’s formula, principle of isolated zeros, uniqueness, principle of identity, and binomial series.
    [Show full text]
  • Math 336 Sample Problems
    Math 336 Sample Problems One notebook sized page of notes will be allowed on the test. The test will cover up to section 2.6 in the text. 1. Suppose that v is the harmonic conjugate of u and u is the harmonic conjugate of v. Show that u and v must be constant. ∞ X 2 P∞ n 2. Suppose |an| converges. Prove that f(z) = 0 anz is analytic 0 Z 2π for |z| < 1. Compute lim |f(reit)|2dt. r→1 0 z − a 3. Let a be a complex number and suppose |a| < 1. Let f(z) = . 1 − az Prove the following statments. (a) |f(z)| < 1, if |z| < 1. (b) |f(z)| = 1, if |z| = 1. 2πij 4. Let zj = e n denote the n roots of unity. Let cj = |1 − zj| be the n − 1 chord lengths from 1 to the points zj, j = 1, . .n − 1. Prove n that the product c1 · c2 ··· cn−1 = n. Hint: Consider z − 1. 5. Let f(z) = x + i(x2 − y2). Find the points at which f is complex differentiable. Find the points at which f is complex analytic. 1 6. Find the Laurent series of the function z in the annulus D = {z : 2 < |z − 1| < ∞}. 1 Sample Problems 2 7. Using the calculus of residues, compute Z +∞ dx 4 −∞ 1 + x n p0(z) Y 8. Let f(z) = , where p(z) = (z −z ) and the z are distinct and zp(z) j j j=1 different from 0. Find all the poles of f and compute the residues of f at these poles.
    [Show full text]
  • Undefinedness and Soft Typing in Formal Mathematics
    Undefinedness and Soft Typing in Formal Mathematics PhD Research Proposal by Jonas Betzendahl January 7, 2020 Supervisors: Prof. Dr. Michael Kohlhase Dr. Florian Rabe Abstract During my PhD, I want to contribute towards more natural reasoning in formal systems for mathematics (i.e. closer resembling that of actual mathematicians), with special focus on two topics on the precipice between type systems and logics in formal systems: undefinedness and soft types. Undefined terms are common in mathematics as practised on paper or blackboard by math- ematicians, yet few of the many systems for formalising mathematics that exist today deal with it in a principled way or allow explicit reasoning with undefined terms because allowing for this often results in undecidability. Soft types are a way of allowing the user of a formal system to also incorporate information about mathematical objects into the type system that had to be proven or computed after their creation. This approach is equally a closer match for mathematics as performed by mathematicians and has had promising results in the past. The MMT system constitutes a natural fit for this endeavour due to its rapid prototyping ca- pabilities and existing infrastructure. However, both of the aspects above necessitate a stronger support for automated reasoning than currently available. Hence, a further goal of mine is to extend the MMT framework with additional capabilities for automated and interactive theorem proving, both for reasoning in and outside the domains of undefinedness and soft types. 2 Contents 1 Introduction & Motivation 4 2 State of the Art 5 2.1 Undefinedness in Mathematics .
    [Show full text]
  • Complex Analysis Class 24: Wednesday April 2
    Complex Analysis Math 214 Spring 2014 Fowler 307 MWF 3:00pm - 3:55pm c 2014 Ron Buckmire http://faculty.oxy.edu/ron/math/312/14/ Class 24: Wednesday April 2 TITLE Classifying Singularities using Laurent Series CURRENT READING Zill & Shanahan, §6.2-6.3 HOMEWORK Zill & Shanahan, §6.2 3, 15, 20, 24 33*. §6.3 7, 8, 9, 10. SUMMARY We shall be introduced to Laurent Series and learn how to use them to classify different various kinds of singularities (locations where complex functions are no longer analytic). Classifying Singularities There are basically three types of singularities (points where f(z) is not analytic) in the complex plane. Isolated Singularity An isolated singularity of a function f(z) is a point z0 such that f(z) is analytic on the punctured disc 0 < |z − z0| <rbut is undefined at z = z0. We usually call isolated singularities poles. An example is z = i for the function z/(z − i). Removable Singularity A removable singularity is a point z0 where the function f(z0) appears to be undefined but if we assign f(z0) the value w0 with the knowledge that lim f(z)=w0 then we can say that we z→z0 have “removed” the singularity. An example would be the point z = 0 for f(z) = sin(z)/z. Branch Singularity A branch singularity is a point z0 through which all possible branch cuts of a multi-valued function can be drawn to produce a single-valued function. An example of such a point would be the point z = 0 for Log (z).
    [Show full text]
  • Control Systems
    ECE 380: Control Systems Course Notes: Winter 2014 Prof. Shreyas Sundaram Department of Electrical and Computer Engineering University of Waterloo ii c Shreyas Sundaram Acknowledgments Parts of these course notes are loosely based on lecture notes by Professors Daniel Liberzon, Sean Meyn, and Mark Spong (University of Illinois), on notes by Professors Daniel Davison and Daniel Miller (University of Waterloo), and on parts of the textbook Feedback Control of Dynamic Systems (5th edition) by Franklin, Powell and Emami-Naeini. I claim credit for all typos and mistakes in the notes. The LATEX template for The Not So Short Introduction to LATEX 2" by T. Oetiker et al. was used to typeset portions of these notes. Shreyas Sundaram University of Waterloo c Shreyas Sundaram iv c Shreyas Sundaram Contents 1 Introduction 1 1.1 Dynamical Systems . .1 1.2 What is Control Theory? . .2 1.3 Outline of the Course . .4 2 Review of Complex Numbers 5 3 Review of Laplace Transforms 9 3.1 The Laplace Transform . .9 3.2 The Inverse Laplace Transform . 13 3.2.1 Partial Fraction Expansion . 13 3.3 The Final Value Theorem . 15 4 Linear Time-Invariant Systems 17 4.1 Linearity, Time-Invariance and Causality . 17 4.2 Transfer Functions . 18 4.2.1 Obtaining the transfer function of a differential equation model . 20 4.3 Frequency Response . 21 5 Bode Plots 25 5.1 Rules for Drawing Bode Plots . 26 5.1.1 Bode Plot for Ko ....................... 27 5.1.2 Bode Plot for sq ....................... 28 s −1 s 5.1.3 Bode Plot for ( p + 1) and ( z + 1) .
    [Show full text]
  • Division by Zero in Logic and Computing Jan Bergstra
    Division by Zero in Logic and Computing Jan Bergstra To cite this version: Jan Bergstra. Division by Zero in Logic and Computing. 2021. hal-03184956v2 HAL Id: hal-03184956 https://hal.archives-ouvertes.fr/hal-03184956v2 Preprint submitted on 19 Apr 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. DIVISION BY ZERO IN LOGIC AND COMPUTING JAN A. BERGSTRA Abstract. The phenomenon of division by zero is considered from the per- spectives of logic and informatics respectively. Division rather than multi- plicative inverse is taken as the point of departure. A classification of views on division by zero is proposed: principled, physics based principled, quasi- principled, curiosity driven, pragmatic, and ad hoc. A survey is provided of different perspectives on the value of 1=0 with for each view an assessment view from the perspectives of logic and computing. No attempt is made to survey the long and diverse history of the subject. 1. Introduction In the context of rational numbers the constants 0 and 1 and the operations of addition ( + ) and subtraction ( − ) as well as multiplication ( · ) and division ( = ) play a key role. When starting with a binary primitive for subtraction unary opposite is an abbreviation as follows: −x = 0 − x, and given a two-place division function unary inverse is an abbreviation as follows: x−1 = 1=x.
    [Show full text]
  • Complex Logarithm
    Complex Analysis Math 214 Spring 2014 Fowler 307 MWF 3:00pm - 3:55pm c 2014 Ron Buckmire http://faculty.oxy.edu/ron/math/312/14/ Class 14: Monday February 24 TITLE The Complex Logarithm CURRENT READING Zill & Shanahan, Section 4.1 HOMEWORK Zill & Shanahan, §4.1.2 # 23,31,34,42 44*; SUMMARY We shall return to the murky world of branch cuts as we expand our repertoire of complex functions when we encounter the complex logarithm function. The Complex Logarithm log z Let us define w = log z as the inverse of z = ew. NOTE Your textbook (Zill & Shanahan) uses ln instead of log and Ln instead of Log . We know that exp[ln |z|+i(θ+2nπ)] = z, where n ∈ Z, from our knowledge of the exponential function. So we can define log z =ln|z| + i arg z =ln|z| + i Arg z +2nπi =lnr + iθ where r = |z| as usual, and θ is the argument of z If we only use the principal value of the argument, then we define the principal value of log z as Log z, where Log z =ln|z| + i Arg z = Log |z| + i Arg z Exercise Compute Log (−2) and log(−2), Log (2i), and log(2i), Log (−4) and log(−4) Logarithmic Identities z = elog z but log ez = z +2kπi (Is this a surprise?) log(z1z2) = log z1 + log z2 z1 log = log z1 − log z2 z2 However these do not neccessarily apply to the principal branch of the logarithm, written as Log z. (i.e. is Log (2) + Log (−2) = Log (−4)? 1 Complex Analysis Worksheet 14 Math 312 Spring 2014 Log z: the Principal Branch of log z Log z is a single-valued function and is analytic in the domain D∗ consisting of all points of the complex plane except for those lying on the nonpositive real axis, where d 1 Log z = dz z Sketch the set D∗ and convince yourself that it is an open connected set.
    [Show full text]
  • 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.
    [Show full text]