Work in the real Hilbert space , with and . The penalty is a proper convex function: it is finite at zero, and the bounded-variation space domain is convex. Its subdifferential at a finite-penalty consists of satisfying for every .
If , expand the quadratic term and use the subgradient inequality:
Thus is the unique minimizer.
Conversely, let be a minimizer. Its penalty is finite since comparison with zero gives a finite objective. For any with , set , where . Convexity gives . Minimality and quadratic expansion then imply
Letting yields . The inequality is automatic if . Hence
This also follows from the subdifferential sum rule, since the quadratic term is everywhere continuous and differentiable, and the subgradient optimality condition. The direct proof above needs no unproved existence theorem.
On the entire plane, the printed global BV domain is not closed in the L2 geometry. The usual definition includes an condition. For , belongs to , has finite distributional variation , and fails to belong to . The truncated lies in and tends to in . Its variation is the interior variation plus , and tends to a finite limit. Thus stays bounded but the literal , establishing failure of L2 closure of the global BV domain. One must not infer universal minimizer existence from an inapplicable closed-penalty proximal operator theorem. The standard closed extension uses the homogeneous bounded-variation space, allowing finite distributional variation without global . The optimality equivalence just proved is valid for the literal penalty whenever a minimizer exists; the next datum has an explicit certified minimizer in its domain.