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Function Spaces and Their Norms

Learning Goals

  1. What is a Banach space?

  2. The spaces of continuous, differentiable and integrable functions.

  3. What do LpL^p norms measure? Intuition via bump functions.

  4. The Rainbow of function spaces.

Banach Spaces

Spaces of Continuous and Differentiable Functions

CC^\infty is not a Banach space — it is a Fréchet space

C(Ω)C^\infty(\overline{\Omega}) is not a Banach space because no single norm can capture its topology. It is instead a Fréchet space: a complete, metrizable, locally convex topological vector space whose topology is defined by a countable family of seminorms (here, Ck\|\cdot\|_{C^k} for k=0,1,2,k = 0, 1, 2, \dots).

Why can’t a single norm work? For any fixed kk, there exist sequences in CC^\infty that converge in CkC^k but diverge in Ck+1C^{k+1}. For example, on [0,1][0,1]:

fn(x)=sin(nx)nf_n(x) = \frac{\sin(n x)}{n}

converges to 0 in C0C^0 (fn=1/n0\|f_n\|_\infty = 1/n \to 0), but fn(x)=cos(nx)f_n'(x) = \cos(nx) does not converge at all — the C1C^1 norms satisfy fnC11\|f_n\|_{C^1} \geq 1 for all nn. No matter which CkC^k norm you pick, there is always a “higher” derivative direction that it fails to control.

A Banach space norm would have to simultaneously control all derivatives, but controlling the kk-th derivative imposes constraints that are strictly independent of controlling the (k1)(k-1)-th. Formally, CC^\infty is not normable: by Kolmogorov’s criterion, a locally convex space admits a norm if and only if it has a bounded neighborhood of zero, and in CC^\infty every neighborhood of zero contains functions with arbitrarily large kk-th derivatives for sufficiently large kk.

Despite not being Banach, CC^\infty is complete in its Fréchet topology (a Cauchy sequence in every CkC^k norm converges in every CkC^k norm), and much of the Banach space theory — including versions of the Open Mapping Theorem and Closed Graph Theorem — extends to Fréchet spaces.

What Do CkC^k Norms Measure?

The CkC^k norm controls the function and its first kk derivatives uniformly. This has a concrete geometric meaning:

The key point: higher-order CkC^k norms are strictly stronger. A sequence can converge in C0C^0 (graphs converge) without converging in C1C^1 (slopes may oscillate wildly).

Proof sketch:

Let {fn}\{f_n\} be Cauchy in CkC^k. Then for each αk|\alpha| \leq k, the sequence {Dαfn}\{D^\alpha f_n\} is Cauchy in C0C^0 (sup-norm). Since (C0,)(C^0, \|\cdot\|_\infty) is complete, each DαfnD^\alpha f_n converges uniformly to some continuous function gαg_\alpha. A standard argument (integrating and differentiating under the limit) shows gα=Dαg0g_\alpha = D^\alpha g_0, so g0Ckg_0 \in C^k and fng0f_n \to g_0 in CkC^k.

Spaces of Integrable Functions

Why equivalence classes?

Strictly speaking, elements of LpL^p are not functions but equivalence classes of functions that agree almost everywhere. This quotient is essential: without it, p\|\cdot\|_p is only a seminorm, not a norm. The problem is that fp=0\|f\|_p = 0 does not imply f=0f = 0 pointwise—only f=0f = 0 a.e. For instance, the function that equals 1 at a single point and 0 elsewhere has LpL^p norm zero but is not the zero function. Passing to equivalence classes identifies all such functions with the zero class, making fp=0    f=0\|f\|_p = 0 \implies f = 0 in LpL^p.

This is a fundamental difference from CkC^k spaces, where functions are determined by their pointwise values and the sup-norm is a genuine norm without any quotienting.

What Do LpL^p Norms Measure?

Different norms capture different features of a function. To build intuition, consider a smooth bump function ϕCc(R)\phi \in \mathcal{C}^\infty_c(\mathbb{R}) with ϕ0\phi \geq 0, supp(ϕ)[1,1]\operatorname{supp}(\phi) \subset [-1, 1], and ϕ=1\|\phi\|_\infty = 1.

We construct two families of functions that independently control height and width.

Scaling the height. Define fA(x)=Aϕ(x)f_A(x) = A\,\phi(x) for A>0A > 0. This rescales the amplitude without changing the support. Then:

fA=A,fA1=Aϕ1,fAp=Aϕp.\|f_A\|_\infty = A, \qquad \|f_A\|_1 = A\|\phi\|_1, \qquad \|f_A\|_p = A\|\phi\|_p.

All norms see the height equally — no surprise, since we are simply rescaling the function values.

Scaling the width. Define gR(x)=ϕ(x/R)g_R(x) = \phi(x/R) for R>0R > 0. This stretches the support to [R,R][-R, R] without changing the peak value. Then:

gR=ϕ=1\|g_R\|_\infty = \|\phi\|_\infty = 1

is independent of RR — the LL^\infty norm is completely insensitive to how wide the function is. On the other hand, by a change of variables y=x/Ry = x/R:

gRpp=gR(x)pdx=Rϕ(y)pdy=Rϕpp\|g_R\|_p^p = \int |g_R(x)|^p \, dx = R \int |\phi(y)|^p \, dy = R\,\|\phi\|_p^p

so that gRp=R1/pϕp\|g_R\|_p = R^{1/p}\|\phi\|_p, which grows as we widen the support.

LpL^p is a Banach Space

The Rainbow of Function Spaces