Prox-regular functions are variationally s-convex or have tilt-stable minimizers exactly when their quadratic bundles satisfy a uniform quadratic lower bound, without requiring subdifferential continuity.
On second-order variational analysis of variational convexity of prox-regular functions
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abstract
Variational convexity, together with ist strong counterpart, of extended-real-valued functions has been recently introduced by Rockafellar. In this paper we present second-order characterizations of these properties, i.e., conditions using first-order generalized derivatives of the subgradient mapping. Up to now, such characterizations are only known under the assumptions of prox-regularity and subdifferential continuity and in this paper we discard the latter. To this aim we slightly modify the definitions of the generalized derivatives to be compatible with the $f$-attentive convergence appearing in the definition of subgradients. We formulate our results in terms of both coderivatives and subspace containing derivatives. We also give formulas for the exact bound of variational convexity and study relations between variational strong convexity, tilt-stable local minimizers and strong metric regularity of some truncation of the subgradient mapping.
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Characterizations of Variational Convexity and Tilt Stability via Quadratic Bundles
Prox-regular functions are variationally s-convex or have tilt-stable minimizers exactly when their quadratic bundles satisfy a uniform quadratic lower bound, without requiring subdifferential continuity.