Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization

• URL: http://arxiv.org/abs/2506.02504v1
• Date: Tue, 03 Jun 2025 06:31:59 GMT
• Title: Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization
• Authors: Xingyu Chen, Bokun Wang, Ming Yang, Quanqi Hu, Qihang Lin, Tianbao Yang,
• Abstract summary: We propose a new state-of-the-art complexity of $O(/epsilon)$ for finding an (nearly) $'level KKT solution.<n>By applying our hinge-of-the-art complexity of $O(/epsilon)$ for finding an (nearly) $'level KKT solution, we achieve a new state-of-the-art complexity of $O(/epsilon)$ for finding an (nearly) $'level KKT solution.
• Score: 64.99236464953032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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