A metric space is compact when every sequence in has a subsequence converging in (the Bolzano–Weierstrass property). A subset is compact when it is so with the induced distance.
Examples
Example 4.17 (Heine’s theorem, with and without compactness)
On , the function is uniformly continuous — Heine says so with no computation, but the direct estimate is instructive:
so works for every point at once. On the same function is not uniformly continuous: with and , the gap while : no single serves . The mechanism is visible: the local Lipschitz constant is bounded on a compact and unbounded on — Heine’s theorem is exactly the statement that compactness caps such local constants uniformly.
Example 4.19 (Distances between sets: compactness earns its keep)
Let be compact, closed, in a metric space. Then
the function is continuous (Exercise 4.11) and positive on ( would put ), so it attains a positive minimum on the compact (Theorem 4.16 (3)). Compactness is not decorative: for two closed sets the infimum can vanish without being attained — in , the hyperbola and the axis are disjoint closed sets with (the points approach the axis). Escape to infinity is exactly what compactness forbids.
Example 4.21 (An -net, counted)
Total boundedness (from the proof of Theorem 4.20) is very concrete on : for , the balls centered at of radius cover it — about balls, and no cover can do with fewer than of them (each ball covers length at most ). In the count squares to order : covering numbers grow like in dimension — a quantitative face of compactness, and the reason the infinite-dimensional unit balls of Chapter 5 (where no finite -net exists at all) cannot be compact.