Mollification of a Lipschitz Convex Function: Smooth Convex Approximations with Bounded Gradients Converging Where the Subgradient is Unique
lemmaAnalysislem:mollified-convex-gradient-convergence-rn-2026aThe mollifications of a convex function that is Lipschitz with a given constant are smooth, convex and Lipschitz with the same constant, with gradients bounded by it; at a point where the subdifferential is a single vector the gradients of the mollifications converge to that vector as the mollification radius tends to zero.
We work in the setting of Euclidean Space and Lebesgue Measure: Standing Notation, whose notation is fixed for every dimension and is used here with a natural number satisfying : the real numbers and sequences, the Euclidean norm , dot product, distance , topology and closed balls, and the Lebesgue measure , are as fixed there. The space is open in itself by claim 1 of Euclidean Space is Open in Itself, and Maps are Continuous and is convex. Derivatives of a function of class on an open subset of are those of Differential Calculus and Convexity on Euclidean Open Sets: Standing Notation §derivatives, and smooth is smoothness on .
Let be a nonnegative real number and let be convex on and Lipschitz with constant from to with the absolute-value metric, with subdifferential . Let be a positive real number and let be a mollifier kernel of radius on . For a positive real number let be given by , a mollifier kernel of radius by Rescaling a Mollifier Kernel; since is continuous on and is continuous and vanishes at every with , the convolution is defined on the whole of , the set being .
1. (Regularity of the mollifications)¶ Let be a positive real number. Then is smooth on , convex on , and Lipschitz with constant , and
2. (Convergence of the gradients)¶ Let and satisfy , and let be a sequence of positive real numbers converging to . Then the sequence converges to in .
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