Convex Relaxation of Discrete Vector-Valued Optimization Problems

We consider a class of in nite-dimensional optimization problems in which a distributed vector-valued variable should pointwise almost everywhere take values from a given nite set M ⊂ R . Such hybrid discrete–continuous problems occur in, e.g., topology optimization or medical imaging and are challenging due to their lack of weak lower semicontinuity. To circumvent this di culty, we introduce as a regularization term a convex integral functional with an integrand that has a polyhedral epigraph with vertices corresponding to the values ofM; similar to the L1 norm in sparse regularization, this “vector multibang penalty” promotes solutions with the desired structure while allowing the use of tools from convex optimization for the analysis as well as the numerical solution of the resulting problem. We show well-posedness of the regularized problem and analyze stability properties of its solution in a general setting. We then illustrate the approach for three speci c model optimization problems of broader interest: optimal control of the Bloch equation, optimal control of an elastic deformation, and a multimaterial branched transport problem. In the rst two cases, we derive explicit characterizations of the penalty and its generalized derivatives for a concrete class of sets M. For the third case, we discuss the algorithmic computation of these derivatives for general sets. These derivatives are then used in a superlinearly convergent semismooth Newton method applied to a sequence of regularized optimization problems. We illustrate the behavior of this approach for the threemodel problemswith numerical examples.