Nonsmooth Riemannian optimization has attracted increasing attention, especially in problems with sparse structures. While existing formulations typically involve convex nonsmooth terms, incorporating nonsmooth difference-of-convex (DC) penalties can enhance recovery accuracy. In this paper, we study a class of nonsmooth Riemannian optimization problems whose objective is the sum of a smooth function and a nonsmooth DC term. We establish, for the first time in the manifold setting, the equivalence between such DC formulations (with suitably chosen nonsmooth DC terms) and their $\ell_0$-regularized or $\ell_0$-constrained counterparts. To solve these problems, we propose an inexact Riemannian proximal DC (iRPDC) algorithmic framework, which returns an $ε$-Riemannian critical point within $\mathcal{O}(ε^{-2})$ outer iterations. Within this framework, we develop several practical algorithms based on different subproblem solvers. Among them, one achieves an overall iteration complexity of $\mathcal{O}(ε^{-3})$, which matches the best-known bound in the literature. In contrast, existing algorithms either lack provable overall complexity or require $\mathcal{O}(ε^{-3})$ iterations in both outer and overall complexity. A notable feature of the iRPDC algorithmic framework is a novel inexactness criterion that not only enables efficient subproblem solutions via first-order methods but also facilitates a linesearch procedure that adaptively captures the local curvature. Numerical results on sparse principal component analysis demonstrate the modeling flexibility of the DC formulaton and the competitive performance of the proposed algorithmic framework.

A preliminary version was submitted to NeurIPS 2024