A Variable Smoothing for Weakly Convex Composite Minimization with Nonconvex Constraint (2412.04225v2)

Abstract: In this paper, we address a nonconvexly constrained nonsmooth optimization problem involving the composition of a weakly convex function and a smooth mapping. To find a stationary point of the target problem, we propose a variable smoothing-type algorithm by combining the ideas of (i) translating the constrained problem into a Euclidean optimization problem with a smooth parametrization of the constraint set; (ii) exploiting a sequence of smoothed surrogate functions, of the cost function, given with the Moreau envelope of a weakly convex function. The proposed algorithm produces a vector sequence by the gradient descent update of a smoothed surrogate function at each iteration. In a case where the proximity operator of the weakly convex function is available, the proposed algorithm does not require any iterative solver for subproblems therein. By leveraging tools in the variational analysis, we show the so-called {\em gradient consistency property}, which is a key ingredient for smoothing-type algorithms, of the smoothed surrogate function used in this paper. Based on the gradient consistency property, we also establish an asymptotic convergence analysis for the proposed algorithm. Numerical experiments demonstrate the efficacy of the proposed algorithm.