Inner Moreau envelope of nonsmooth conic chance constrained optimization problems

TL;DR

This paper introduces a regularization technique using the Moreau envelope for nonsmooth probabilistic constraints in conic optimization, ensuring smoothness and convergence of solutions.

Contribution

It proposes a novel regularization method applying the Moreau envelope to scalarized probabilistic functions, enabling smooth approximations and convergence analysis in nonsmooth chance constrained problems.

Findings

The regularized probability function is shown to be smooth under mild assumptions.

The minimizers of the regularized problems converge to those of the original problem.

Applications include joint, semidefinite, and robust chance constrained optimization.

Abstract

Optimization problems with uncertainty in the constraints occur in many applications. Particularly, probability functions present a natural form to deal with this situation. Nevertheless, in some cases, the resulting probability functions are nonsmooth. This motivates us to propose a regularization employing the Moreau envelope of a scalar representation of the vector inequality. More precisely, we consider a probability function which covers most of the general classes of probabilistic constraints: $$\varphi(x)=\mathbb{P}(\Phi(x,\xi)\in -\mathcal{K}),$$ where $\mathcal{K}$ is a convex cone of a Banach space. The conic inclusion $\Phi (x,\xi) \in - \mathcal{K}$ represents an abstract system of inequalities, and $\xi$ is a random vector. We propose a regularization by applying the Moreau envelope to the scalarization of the function $\Phi$. In this paper, we demonstrate, under mild assumptions, the smoothness of such a regularization and that it satisfies a type of variational convergence to the original probability function. Consequently, when considering an appropriately structured problem involving probabilistic constraints, we can thus entail the convergence of the minimizers of the regularized approximate problems to the minimizers of the original problem. Finally, we illustrate our results with examples and applications in the field of (nonsmooth) joint, semidefinite and probust chance constrained optimization problems.