Generalized Differentiability and Second-Order Necessary Optimality Conditions for an Elliptic Optimal Control Problem with Exponential Nonlinearity and Discrete Measures

TL;DR

This paper develops generalized differentiability and second-order optimality conditions for an elliptic control problem with exponential nonlinearity and discrete measures, addressing challenges due to non-differentiability of the control-to-state operator.

Contribution

It introduces a finite-dimensional directional differentiability framework and a generalized derivative concept for the control-to-state operator in complex elliptic control problems.

Findings

Established finite-dimensional directional differentiability of the control-to-state operator.

Derived first- and second-order necessary optimality conditions.

Provided estimates for Taylor expansions of the exponential Nemytskii operator.

Abstract

This paper deals with generalized differentiability and second-order necessary optimality conditions for a box-constrained optimal control problem governed by an exponential semilinear elliptic equation with discrete measures as sources, where the control belongs to the space of absolutely summable sequences. The presence of the exponential nonlinearity and discrete measures makes the analysis particularly challenging. In particular, the control-to-state operator may fail to be directionally differentiable. To address this issue, we first establish finite-dimensional directional differentiability of the control-to-state operator; that is, the operator is directionally differentiable along directions contained in finite-dimensional subspaces of the control space. We then introduce a notion of generalized derivative defined as the limit of the associated finite-dimensional directional derivatives as the dimension of these subspaces tends to infinity. Based on this concept, together with estimates for first- and second-order Taylor-type expansions of the exponential Nemytskii operator associated with the control-to-state mapping, we derive first- and second-order generalized differentiability of the reduced objective functional. This leads to first- and second-order necessary optimality conditions for the optimal control problem.