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LemmaStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)
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Convexity, lower semicontinuity, and BV agreement of L² TV

Statement

Let Ω⊆R2 be open and let TV2(⋅;Ω) be the extended distributional total variation on L2(Ω;R). It is proper, convex, lower semicontinuous in the L2 norm, and positively one-homogeneous for nonnegative scalars. For every u∈BV(Ω)∩L2(Ω), it agrees with spatial TV: TV2(u;Ω)=TV(u;Ω).

Facts & Assumptions

Given: An open Ω⊆R2, the functional TV2 on L2(Ω;R), and the spatial BV total variation.

[F1]

Distributional TV as an extended functional on L²: TV2(u;Ω) is the supremum of ∫Ωu div⁡φ over smooth compactly supported vector fields with Euclidean norm at most one.

[F2]

Test-field formula and L1 lower semicontinuity of spatial TV: for u∈BV(Ω), TV(u;Ω) equals the same test-field supremum.

[F3]

Cauchy–Schwarz: ∣⟨x,y⟩∣≤∥x∥ ∥y∥, with equality exactly for dependent pairs: each test functional u↦∫u div⁡φ is continuous on L2, with absolute value at most ∥u∥2∥div⁡φ∥2.

Proof

1.1F1givenalgebra

The zero function has TV2(0;Ω)=0, so the functional is proper; its defining test class is symmetric under φ↦−φ, hence its supremum is nonnegative. For λ>0, linearity of every test integral gives TV2(λu;Ω)=λTV2(u;Ω), and the equality also holds for λ=0. For θ∈[0,1], every admissible field gives ∫(θu+(1−θ)v)div⁡φ≤θTV2(u;Ω)+(1−θ)TV2(v;Ω); taking the supremum proves convexity, with the extended-real cases automatic.

2.1F1F2F3step 1.1∎

By [F3], every fixed test integral is a continuous linear functional on L2. For each real a, the sublevel set {u:TV2(u;Ω)≤a} is the intersection, over all admissible φ, of the closed half-spaces {u:∫udiv⁡φ≤a}; hence it is closed and TV2 is lower semicontinuous. If u∈BV(Ω)∩L2(Ω), [F1] and [F2] give TV2(u;Ω)=TV(u;Ω).

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