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Fourier Analysis Notes, Spring 2020

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Fourier Analysis Notes, Spring 2020 Fourier Analysis Notes, Spring 2020 Peter Woit Department of Mathematics, Columbia University [email protected] September 3, 2020 2 Contents 1 Introduction 5 1.1 Fourier series . .5 1.2 The Fourier transform . .7 2 The Poisson Summation Formula, Theta Functions, and the Zeta Function 9 2.1 The Poisson summation formula and some applications . .9 2.1.1 The heat kernel . 10 2.1.2 The Poisson kernel . 11 2.2 Theta functions . 12 2.2.1 The Jacobi theta function . 13 2.3 The Riemann zeta function . 15 2.3.1 The Mellin transform . 16 2.3.2 The zeta function and the Mellin transform of the theta function . 18 2.3.3 The functional equation for the zeta function . 19 3 Distributions 23 3.1 Introduction . 23 3.2 Distributions: definition . 24 3.3 Distributions: examples . 25 3.4 The derivative of a distribution . 28 3.4.1 The transpose of a linear transformation . 29 3.4.2 Translations . 29 3.4.3 The derivative . 30 3.5 The Fourier transform of a distribution . 33 3.6 Convolution of a function and a distribution . 38 3.7 Distributional solutions of differential equations . 41 4 Higher dimensions 43 4.1 Introduction . 43 4.2 Fourier series and the Fourier transform for d > 1 . 43 4.2.1 Fourier series for d > 1.................... 43 4.2.2 The Fourier transform for d > 1............... 44 3 4.3 Rotations and the Fourier transform . 45 4.3.1 Two dimensions . 47 4.3.2 Three dimensions . 48 5 Wave Equations 53 5.1 Introduction . 53 5.2 The heat and Schr¨odingerequations in higher dimensions . 53 5.3 The wave equation in d =1..................... 53 5.4 The wave equation in d =3..................... 59 5.5 The wave equation in d =2..................... 61 6 The finite Fourier transform 65 6.1 Introduction . 65 6.2 The group Z(N)........................... 65 6.3 Fourier analysis on Z(N)...................... 66 6.4 Fourier analysis on commutative groups . 69 6.5 Fourier analysis on Z∗(q)...................... 73 6.6 The zeta function, primes and Dirichlet's theorem . 76 4 Chapter 1 Introduction These are notes from the second half of a spring 2020 Fourier analysis class, written up since the class turned into an online class for the second half of the semester due to the COVID pandemic. The course to some degree followed the textbook [3], with additional material on distributions from other sources. The first part of the course discussed the basic theory of Fourier series and Fourier transforms, with the main application to finding solutions of the heat equation, the Schr¨odinger equation and Laplace's equation. For the Fourier series, we roughly followed chapters 2, 3 and 4 of [3], for the Fourier transform, sections 5.1 and 5.2 . An alternate more detailed source that is not qute as demanding on the students is the first half of the book by Howell, [1]. A quick summary of this material follows. 1.1 Fourier series The subject of Fourier series deals with complex-valued periodic functions, or equivalently, functions defined on a circle. Taking the period or circumference of the circle to be 2π, the Fourier coefficients of a function are 1 Z π fb(n) = f(θ)e−inθdθ 2π −π and the Fourier series for the function is 1 X fb(n)einθ n=−∞ A surprisingly difficult central problem of the subject is that of pointwise- convergence. For what conditions on f does the Fourier series at θ converge to f(θ)? It turns out that differentiability of f is sufficient to imply pointwise- convergence (which we proved), but continuity is not sufficient (we did not prove this). We also looked at examples of the convergence behavior at a jump discontinuity (the \Gibbs phenomenon"). 5 One can consider the Hermitian inner products 1 Z π hf; gi = f(θ)g(θ)dθ 2π −π on functions and on sets of Fourier coefficients 1 X hffbg; fgbgi = fb(n)gb(n) n=−∞ and ask instead about norm-convergence of the Fourier series (i.e. does the norm of the difference between f and its Fourier series converge to zero?). The answer here is simpler than that for pointwise convergence. Functions with finite norm have norm-convergent Fourier series, with the map taking f to the set ffbg of its Fourier coefficients a unitary (inner-product preserving) isomorphism. The discussion of this subject provided an opportunity to explain the limitations of the Riemann integral and advertise (without defining it) the Lebesgue integral. The convolution product f ∗ g on periodic functions was defined, showing that it corresponds to the pointwise product on Fourier coefficients. Given a function g(θ) on the circle, Fourier series were used to find solutions of partial differential equations: f(t; θ) = g ∗ Ht(θ) with Ht(θ) the heat kernel solves the heat equation @ @2 f = f @t @θ2 for f(0; θ) = g(θ) and t ≥ 0 f(t; θ) = g ∗ St(θ) with St(θ) the Schr¨odingerkernel (an imaginary time version of the heat kernel) solves the Schr¨odingerequation @ 2 @2 i f = − ~ f ~@t 2m @θ2 for f(0; θ) = g(θ) and all t. f(r; θ) = g ∗ Pr(θ) with Pr(θ) the Poisson kernel solves the Laplace equation ∆f = 0 for f(1; θ) = g(θ) and 0 ≤ r ≤ 1 6 1.2 The Fourier transform Turning from functions on the circle to functions on R, one gets a more sym- metrical situation, with the Fourier coefficients of a function f now replaced by another function on R, the Fourier transform fe, given by Z 1 fe(p) = f(x)e−2πipxdx −∞ The analog of the Fourier series is the integral Z 1 f(x) = fe(p)e2πipxdx −∞ The problem of convergence of the Fourier series is replaced by the problem of understanding which functions f have a well-defined Fourier transform fe, and for which such fe can f be recovered by the second integral above (this is the problem of \Fourier inversion"). We took a different approach to this problem than the textbook [3], first showing that Fourier inversion holds for a certainly highly restricted class S(R) of functions, the Schwartz functions. A function f is in S(R) if it and all its derivatives exist for all x and fall off faster than any power of x. Functions more general than Schwartz functions (i.e. slower fall-off at ±∞, lack of derivatives or discontinuity for some values of x) will be treated as distributions, a topic not covered in [3] but discussed in detail later in these notes. For the Fourier transform one again can define the convolution f ∗ g of two functions, and show that under Fourier transform the convolution product becomes the usual product (fgf)(p) = fe(p)ge(p) The Fourier transform takes differentiation to multiplication by 2πip and one can as in the Fourier series case use this to find solutions of the heat and Schr¨odinger equations (with θ 2 S1 replaced by x 2 R), as well as solutions to the Laplace equation in the upper half-plane. 7 8 Chapter 2 The Poisson Summation Formula, Theta Functions, and the Zeta Function 2.1 The Poisson summation formula and some applications Given a Schwartz function f 2 S(R) on the real number line R, you can con- struct a periodic function by taking the infinite sum 1 X F1(x) = f(x + n) n=−∞ This has the properties The sum converges for any x, since f is falling off at ±∞ faster than any power. F1(x) is periodic with period 1 F1(x + 1) = F1(x) This is because going from x to x+1 just corresponds to a shift of indexing by 1 in the defining sum for F1(x). Since F1(x) is a periodic function, you can treat it by Fourier series methods. Note that the periodicity here is chosen to be 1, not 2π, so you need slightly dif- ferent formulas. For a function g with period 1 whose Fourier series is pointwise convergent, you have Z 1 −i2πnx gb(n) = g(x)e dx 0 9 1 X g(x) = g^(n)ei2πnx n=−∞ If you compute the Fourier coefficients of F1(x) you find Z 1 1 X −i2πmx Fc1(m) = f(x + n)e dx 0 n=−∞ 1 X Z 1 = f(x + n)e−i2πmxdx n=−∞ 0 1 X Z n+1 = f(x + n)e−i2πmxdx n=−∞ n Z 1 = f(x)e−i2πmxdx −∞ = fb(m) Theorem (Poisson Summation Formula). If f 2 S(R) 1 1 X X f(x + n) = fb(n)ei2πnx n=−∞ n=−∞ Proof. The left hand side is the definition of F1(x), the right hand side is its expression as the sum of its Fourier series. What most often gets used is the special case x = 0, with the general case what you get from this when translating by x: Corollary. If f 2 S(R) 1 1 X X f(n) = fb(n) n=−∞ n=−∞ This is a rather remarkable formula, relating two completely different infinite sums: the sum of the values of f at integer points and the sum of the values of its Fourier transform at integer points. 2.1.1 The heat kernel The Poisson summation formula relates the heat kernel on R and on S1. Recall that the formula for the heat kernel on R is 2 1 − x Ht;R(x) = p e 4t 4πt with Fourier transform −4π2p2t Hbt;R(p) = e 10 Applying the Poisson summation formula to Ht;R gives 1 1 X X −4π2n2t 2πinx Ht;R(x + n) = e e = Ht;S1 (x) (2.1) n=−∞ n=−∞ 1 where Ht;S1 is the heat kernel on S .
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