Convex relaxation of discrete vector-valued optimization problems

TL;DR

This paper introduces a convex relaxation technique for infinite-dimensional vector-valued optimization problems with discrete constraints, enabling the use of convex optimization tools for problems like topology optimization and medical imaging.

Contribution

It proposes a vector multibang penalty to relax discrete constraints, analyzes its properties, and demonstrates its application to control and transport problems with a superlinearly convergent Newton method.

Findings

Well-posedness and stability of the regularized problem

Explicit characterization of the penalty for specific models

Numerical examples demonstrating the approach's effectiveness

Abstract

We consider a class of infinite-dimensional optimization problems in which a distributed vector-valued variable should pointwise almost everywhere take values from a given finite set $\mathcal{M}\subset\mathbb{R}^m$. 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 difficulty, we introduce as a regularization term a convex integral functional with an integrand that has a polyhedral epigraph with vertices corresponding to the values of $\mathcal{M}$; similar to the $L^1$ 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 specific 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 first two cases, we derive explicit characterizations of the penalty and its generalized derivatives for a concrete class of sets $\mathcal{M}$. 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 three model problems with numerical examples.