Chapter 5: Application: Fourier Series

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1 Chapter 5: Application: Fourier Series For lack of time, this chapter is only an outline of some applications of Functional Analysis and some proofs are not complete. 5.1 Definition. If f L 1 [, 2π], then the Fourier coefficients of f are ˆf(n) = 1 2π e int f(t) dt 2π (n Z). The Fourier series of f is n= ˆf(n)e int. 5.2 Remark. Using the fact that the functions φ n (t) = 1 2π e int (n Z) form an orthonormal basis for L 2 [, 2π] we can conclude that, for f L 2 [, 2π], f = n Z f, φ n φ n independent of the order of the φ n and with convergence in the norm of L 2 [, 2π]. See example As f, φ n φ n (t) = ˆf(n)e int, we see that the Fourier series of a function f L 2 [, 2π] converges to f in L 2 norm. The coefficients ˆf(n) can be defined for f L 1 [, 2π] (recall L 2 [, 2π] L 1 [, 2π]), but there is no guarantee that the series converges to the function in any sense. However it is true that the Fourier series (or the sequence of Fourier coefficients ˆf(n), n Z) uniquely determines the function f L 1 [, 2π] (see Theorem 5.8) even though the way the Fourier series of f L 1 [, 2π] determines the function f is not very straightforward. 5.3 Theorem (Riemann-Lebesgue Lemma). If f L 1 [, 2π], then lim n ˆf(n) =. 1

2 2 Chapter 5: Application: Fourier Series Proof. Some of the details are omitted here, but the idea is based on the observation that the result is quite obvious when f is a trigonometric polynomial. These polynomials are dense in L 1 [, 2π] (and each trigonometric polynomial p has only finitely many nonzero Fourier coefficients, hence ˆp(n) = for n large). We can define a linear operator T : L 1 [, 2π] l T (f) = ( ˆf(), ˆf(1), ˆf( 1), ˆf(2), ˆf( 2),...). To check that T (f) l when f L 1 [, 2π] use the fact that (for each n Z) ˆf(n) 1 2π 2π 2π e int f(t) dt 1 f(t) dt 2π using e int = 1 = 1 2π f 1. So T (f) = sup n ˆf(n) < and T (f) l. By linearity of the integral, we can see that T is linear and then the inequality T (f) (1/2π) f 1 shows that T is a bounded operator. As T maps the trigonometric polynomials into c, and c l is closed, it follows (using density of the trigonometric polynomials in L 1 [, 2π]) that the values of T are all in c. [The idea is that if f L 1 [, 2π], there is a sequence (p n ) n=1 of trigonometric polynomials with lim n p n = f (in the norm 1 ). By continuity of T, T (f) = lim n T (p n ) in l. As T (p n ) c for each n, and c is closed in l, we must have t(f) c.] We have not proved that the trigonometric polynomials are actually dense in L 1 [, 2π]. The proof of that is not so different to the proof that the same trigonometric polynomials are dense in L 2 [, 2π]. Some indication of how a proof can go was in Example Corollary. The map T : L 1 [, 2π] c given by is a bounded linear operator. T (f) = ( ˆf(), ˆf(1), ˆf( 1), ˆf(2), ˆf( 2),...)

3 The corollary is essentially a restatement of the Riemann-Lebesgue Lemma, or of the way we proved it. 5.5 Definition. The Dirichlet kernels are D n (t) = n e ijt (n =, 1, 2,...). j= n 5.6 Remark. This is related to Fourier series because the partial sums of the Fourier series of f L 1 [, 2π] are given by n j= n ˆf(j)e ijt = 1 2π D n (t θ)f(θ) dθ. 2π This formula is easy to verify and the integral involved is known as a convolution (of f and D n ). We sometimes write S n f for the function (S n f)(t) = n j= n ˆf(j)e ijt and refer to S n f as the nth partial sum of the Fourier series of f. The partial sum operator is a linear operator on functions. We can say S n : L 1 [, 2π] L 1 [, 2π] or we can regard S n as having its values in a nicer space than L 1 [, 2π], such as the 2π-periodic continuous functions on [, 2π]. 5.7 Lemma. D n 1 as n. This is proved by a fairly direct calculation to show that D n (t) = sin(n )t sin 1 2 t and then estimating the integral in a somewhat careful way. 5.8 Theorem. The map T of Corollary 5.4 is injective but not surjective.

4 4 Chapter 5: Application: Fourier Series This Theorem implies that the Fourier coefficients of a function f L 1 [, 2π] determine f completely, but that the Riemann-Lebesgue lemma is not a full description of the possible sequences of Fourier coefficients for such f. The proof of injectivity relies on Lusin s theorem and the Weierstrass theorem to show that if ˆf(n) = for all n Z, then f(t) dt = (1) E for all measurable subsets E [, 2π]. From ˆf(n) = for all n we quickly see that 2π f(t)p(t) dt = for all trigonometric polynomials p and then from the Weierstrass theorem it follows easily that 2π f(t)g(t) dt = for all continuous 2π-periodic functions g. From Lusin s theorem we can get a sequence g n of such continuous 2π-periodic functions so that lim g n(t) = m { 1 if t E if t / E for almost every t [, 2π]. Moreover we can get such a sequence where sup t g n (t) = g n 1 for all n. By the Lebesgue dominated convergence theorem we get (1). If f is not zero in L 1 [, 2π] then it must have nonzero real or imaginary part. Then we can find δ = 1/k > so that one of the following sets E has positive measure: E = {t [, 2π] : Rf(t) > δ} E = {t [, 2π] : Rf(t) < δ} E = {t [, 2π] : If(t) > δ} E = {t [, 2π] : If(t) < δ} In all cases equation (1) leads to a contradiction. This way we can show that T is injective. If it was surjective then it would be a bijective bounded linear operator between Banach spaces and so the open mapping theorem would say that its inverse T 1 would have to be bounded from c to L 1 [, 2π]. But that is not the case because from Lemma 5.7 one can see T D n = 1 in c but T 1 (T D n ) 1 = D n 1 as n. The next result aims to show that Fourier series are not so well-behaved even for continuous 2π-periodic functions.

5 Theorem. There exists a continuous 2π-periodic function f such that the partial sums of its Fourier series do not converge at t =, that is such that does not exist. lim (S nf)() n Proof. This can be shown via the uniform boundedness principle applied to the Banach space CP [, 2π] of all continuous functions f: [, 2π] C with f() = f(2π). (These functions are the restrictions to [, 2π] of continuous 2π-periodic functions f: R C. It is quite easy to see that CP [, 2π] = {f C[, 2π] : f() f(1) = } is a closed linear subspace of C[, 2π]. It is the kernel of the bounded linear functional α: C[, 2π] C given by α(f) = f() f(1). So CP [, 2π] is a Banach space in the norm we usually use on C[, 2π].) We take the supremum norm f = sup t [,2π] f(t) on CP [, 2π] and then it is a Banach space. For each n the linear operator s n : CP [, 2π] C f (S n f)() = 1 2π 2π f(t)d n (t) dt can be seen to have norm s n op = 1 D 2π n 1 <. (This requires some proof, but is not very difficult. To show s n op 1 D 2π n 1, estimate s n (f) by taking the absolute value inside the integral. To show that s n 1 D 2π n 1, take f(t) = D n (t)/ D n (t) and check that f CP [, 2π], f = 1 and s n (f) = 1 D 2π n 1.) If lim n (S n f)() did exist for each f CP [, 2π], then it would also be true that sup (S n f)() < n for each f CP [, 2π]. By the uniform boundedness principle it would then follow that sup n s n op <. But this is false and so there must exist f as required. This type of proof is not constructive. It does not tell you how to find a function f CP [, 2π] with a Fourier series that fails to converge at t =. It only tells you that such a function exists. By the way, there is nothing very special about t =. A similar proof would show that for any θ [, 2π] there is a function f CP [, 2π] such

6 6 Chapter 5: Application: Fourier Series that lim n (S n f)(θ) fails to exist. Actually this also follows from the theorem by making a change of variables. To see the details of these results, look in W. Rudin, Real and Complex Analysis. Richard M. Timoney (March 19, 29)

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