We readdress the classical topic of second-order sufficient optimality conditions for optimization problems with nonsmooth structure. Based on the so-called second subderivative of the objective function and of the indicator function associated with the feasible set, one easily obtains second-order sufficient optimality conditions of abstract form. In order to exploit further structure of the problem, e.g., composite terms in the objective function or feasible sets given as (images of) pre-images of closed sets under smooth transformations, to make these conditions fully explicit, we study calculus rules for the second subderivative under mild conditions. To be precise, we investigate a chain rule and a marginal function rule, which then also give a pre-image and image rule, respectively. As it turns out, the chain rule and the pre-image rule yield lower estimates, desirable in order to obtain sufficient optimality conditions, for free. Similar estimates for the marginal function and the image rule are valid under a comparatively mild inner calmness* assumption. Our findings are illustrated by several examples including problems from composite, disjunctive, and nonlinear second-order cone programming.

Contact details are reproduced from the original publication and may be historical.

Matúš Benko

Institut für Mathematik, Applied Mathematics and Optimization, University of Vienna, Austria

matus.benko@univie.ac.at

Patrick Mehlitz

Institute of Mathematics, Brandenburgische Technische Universität, Cottbus-Senftenberg, Germany

mehlitz@b-tu.de
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M. Benko, P. Mehlitz. “Why Second-Order Sufficient Conditions are, in a Way, Easy – or – Revisiting the Calculus for Second Subderivatives.” Journal of Convex Analysis 30 (2023), No. 2, 541–589. https://doi.org/10.68381/jca30031