Learning Goals¶
What is a Banach space?
The spaces of continuous, differentiable and integrable functions.
What do norms measure? Intuition via bump functions.
The Rainbow of function spaces.
Banach Spaces¶
Spaces of Continuous and Differentiable Functions¶
is not a Banach space — it is a Fréchet space
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, for ).
Why can’t a single norm work? For any fixed , there exist sequences in that converge in but diverge in . For example, on :
converges to 0 in (), but does not converge at all — the norms satisfy for all . No matter which 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 -th derivative imposes constraints that are strictly independent of controlling the -th. Formally, 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 every neighborhood of zero contains functions with arbitrarily large -th derivatives for sufficiently large .
Despite not being Banach, is complete in its Fréchet topology (a Cauchy sequence in every norm converges in every 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 Norms Measure?¶
The norm controls the function and its first derivatives uniformly. This has a concrete geometric meaning:
controls the height: two functions are -close if their graphs are uniformly close.
additionally controls the slope: two functions are -close if their graphs are close and their tangent lines are close at every point.
controls curvature, jerk, and higher-order shape information. Closeness in means the functions “look the same” up to -th order magnification.
The key point: higher-order norms are strictly stronger. A sequence can converge in (graphs converge) without converging in (slopes may oscillate wildly).
Proof sketch:
Let be Cauchy in . Then for each , the sequence is Cauchy in (sup-norm). Since is complete, each converges uniformly to some continuous function . A standard argument (integrating and differentiating under the limit) shows , so and in .
Spaces of Integrable Functions¶
Why equivalence classes?
Strictly speaking, elements of are not functions but equivalence classes of functions that agree almost everywhere. This quotient is essential: without it, is only a seminorm, not a norm. The problem is that does not imply pointwise—only a.e. For instance, the function that equals 1 at a single point and 0 elsewhere has norm zero but is not the zero function. Passing to equivalence classes identifies all such functions with the zero class, making in .
This is a fundamental difference from spaces, where functions are determined by their pointwise values and the sup-norm is a genuine norm without any quotienting.
What Do Norms Measure?¶
Different norms capture different features of a function. To build intuition, consider a smooth bump function with , , and .
We construct two families of functions that independently control height and width.
Scaling the height. Define for . This rescales the amplitude without changing the support. Then:
All norms see the height equally — no surprise, since we are simply rescaling the function values.
Scaling the width. Define for . This stretches the support to without changing the peak value. Then:
is independent of — the norm is completely insensitive to how wide the function is. On the other hand, by a change of variables :
so that , which grows as we widen the support.