Difference-of-Convex Optimization via Inexact Smoothing Descent Methods: Difference of High-Order Moreau Envelopes

This paper studies difference-of-convex (DC) optimization problems through smoothing descent techniques.

In particular, we introduce the difference of high-order Moreau envelopes (HOME-DC) and establish its fundamental and differential properties.

Approximating the underlying proximal points, we generate an inexact first-order oracle for HOME-DC and characterize its accuracy guarantees.

Building upon this oracle, we propose a class of inexact descent methods for minimizing DC functions and provide a convergence analysis.

The proposed framework extends the applicability of envelope-based optimization techniques to a broad class of structured nonconvex problems while accommodating inexact solutions to subproblems.

Preliminary numerical experiments on a sparse clustering problem demonstrate the approach's practical potential and support the theoretical findings.