Stochastic Momentum Methods for Non-smooth Non-Convex Finte-Sum Coupled Compositional Optimization
Xingyu Chen, Bokun Wang, Ming Yang, Qihang Lin, Tianbao Yang
Finite-sum Coupled Compositional Optimization (FCCO), characterized by its coupled compositional objective structure,  emerges as an important optimization paradigm for addressing a wide range of machine learning problems.  In this paper, we focus on a challenging class of non-convex non-smooth FCCO, where the outer functions are non-smooth weakly convex or convex and the inner functions are smooth or weakly convex. Existing state-of-the-art result face two key limitations: (1) a high iteration complexity of  $O(1/\epsilon^6)$  under the assumption that the  stochastic inner functions are Lipschitz continuous in expectation; (2) reliance on vanilla SGD-type updates, which are not suitable for deep learning applications. Our main contributions are two fold: (i) We propose stochastic momentum methods tailored for non-smooth FCCO that come with  provable convergence guarantees; (ii) We establish a **new state-of-the-art** iteration complexity of $O(1/\epsilon^5)$. Moreover, we apply our algorithms to multiple inequality constrained non-convex optimization problems involving smooth or weakly convex functional inequality constraints. By optimizing a smoothed hinge penalty based formulation, we achieve a **new state-of-the-art** complexity of $O(1/\epsilon^5)$ for finding an (nearly) $\epsilon$-level KKT solution. Experiments on three tasks demonstrate the effectiveness of the proposed algorithms.