Extend by infinity outside . For a bounded linear operator , the objective is coercive, weakly lower semicontinuous and strictly convex for , so it has a unique minimizer. The quadratic penalty ensures existence and uniqueness even if has a null space. Its limiting exact solution minimizes in the data fiber, which need not be the minimum-norm least-squares solution.
For fixed and nonzero bounded , the reconstruction vanishes when , which includes a neighborhood of zero. If it were linear it would vanish everywhere. Choose a coordinate with and data with ; the optimality inequality at zero fails, so its minimizer is nonzero. Thus the map is nonlinear. The qualification is essential: gives the zero linear map.
Assume an exact solution of finite penalty exists and select the unique -minimizing exact solution . Minimality gives . If and , boundedness, weak sequential compactness of bounded sequences in a reflexive Banach space and lower semicontinuity identify every weak limit with . The separate lower semicontinuity of and , together with the upper bound on , forces the Hilbert norms to converge. The Radon-Riesz theorem then gives norm convergence.
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