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CorollaryStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-13
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Weak convergence implies lower semicontinuity of the norm

Statement

Assume HB (The real dominated-extension principle as an additional hypothesis over ZF). If a net xi⇀x in a real or complex normed space X, then

∥x∥≤lim inf⁡i∥xi∥:=sup⁡i0inf⁡i≥i0∥xi∥,

where the right side is an extended nonnegative real number. No boundedness of the net is assumed.

Facts & Assumptions

[F1]

Weak convergence gives f(xi)→f(x) for every f∈X∗ (Weak convergence of nets and sequences).

[F2]

Under HB, ∥x∥=max⁡∥f∥≤1∣f(x)∣ (Relative dual norming, point separation, and recovery of the norm).

Proof

Given: HB and a weakly convergent net with specified limit x.

1.1givenF1algebra

Write L=sup⁡i0inf⁡i≥i0∥xi∥. Every tail is nonempty because the index preorder is reflexive and nonempty, so its infimum exists in [0,∞), and their supremum exists in [0,∞]. For f∈X∗ with ∥f∥≤1 and any ε>0, scalar convergence and ∣∣a∣−∣b∣∣≤∣a−b∣ give eventually ∥xi∥≥∣f(xi)∣>∣f(x)∣−ε. Thus one tail infimum is at least ∣f(x)∣−ε, whence L≥∣f(x)∣−ε.

2.1step 1.1F2algebra

If L=∞ the assertion holds. Otherwise letting the positive error decrease shows L≥∣f(x)∣ for every dual unit-ball member: a positive gap is contradicted by half that gap. The HB norm formula yields L≥∥x∥. At x=0 this follows already from L≥0; the zero functional ensures the dual unit ball is nonempty, even for the zero space. □

Depends on

Dependency tree · 2 levels

Sources