Open mapping and closed graph
Let X and Y be Banach spaces and T: X -> Y a bounded surjective operator. The open mapping theorem says T maps open sets to open sets. The striking corollary is the bounded inverse theorem: if T is also a bijection, then T⁻¹ is automatically bounded. Invertibility of a continuous linear bijection between Banach spaces is free continuity of the inverse — something utterly false without completeness.
The closed graph theorem is its twin. The graph of T is { (x, Tx) }. For a linear operator between Banach spaces, T is bounded if and only if its graph is closed. In practice this halves the work of proving continuity: instead of estimating ||Tx||, you only check that whenever x_n -> x and Tx_n -> z, the limit forces z = Tx. You get to assume Tx_n already converges.
Uniform boundedness
The uniform boundedness principle (Banach–Steinhaus) closes the trio. Let (T_α) be a family of bounded operators from a Banach space X to a normed space Y. If for each fixed x the set { ||T_α x|| } is bounded, then the operator norms { ||T_α|| } are bounded uniformly in α. Pointwise control upgrades, for free, to uniform control. Again the proof is a Baire-category argument inside the complete space X.
Application: a pointwise limit of operators is bounded.
Let X, Y be Banach, T_n ∈ B(X, Y), and suppose
T x := lim_n T_n x exists for every x.
Claim: T is a bounded linear operator.
Linearity. Limits respect linear combinations:
T(a x + b y) = lim (a T_n x + b T_n y) = a T x + b T y.
Boundedness. For each fixed x, (T_n x) converges, hence is
bounded, so sup_n ||T_n x|| < ∞. By uniform boundedness,
M := sup_n ||T_n|| < ∞.
Then for every x:
||T x|| = lim_n ||T_n x|| ≤ sup_n ||T_n|| · ||x|| ≤ M ||x||.
So ||T|| ≤ M < ∞: T is bounded. ∎
Note the leap: each T_n having finite norm is obvious, but
that the *common* bound M is finite — uniform over n — is
exactly what Banach–Steinhaus supplies.These three principles, together with Hahn–Banach from guide 4, are the classical pillars of linear functional analysis. Notice the recurring lesson of the whole track: completeness is what converts a soft pointwise or algebraic hypothesis into a hard quantitative bound. That conversion — from existence to estimate — is the deep reason Banach spaces are the right setting.