Revisiting Stochastic Multi-Level Compositional Optimization

作者：Wei Jiang, Sifan Yang, Yibo Wang, Tianbao Yang, Lijun Zhang · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2025 · DOI：10.1109/tpami.2025.3552197 · 被引用次数：1 · 研究领域：Reservoir Engineering and Simulation Methods

This paper explores stochastic multi-level compositional optimization, where the objective function is a composition of multiple smooth functions. Traditional methods for solving this problem suffer from either sub-optimal sample complexities or require huge batch sizes. To address these limitations, we introduce the Stochastic Multi-level Variance Reduction (SMVR) method. In the expectation case, our SMVR method attains the optimal sample complexity of $\mathcal {O}(1/\epsilon ^{3})$O(1/ε3) to find an $\epsilon$ε-stationary point for non-convex objectives. When the function satisfies convexity or the Polyak-Łojasiewicz (PL) condition, we propose a stage-wise SMVR variant. This variant improves the sample complexity to $\mathcal {O}(1/\epsilon ^{2})$O(1/ε2) for convex functions and $\mathcal {O}(1/(\mu \epsilon ))$O(1/(με)) for functions meeting the $\mu$μ-PL condition or $\mu$μ-strong convexity. These complexities match the lower bounds not only in terms of $\epsilon$ε but also in terms of $\mu$μ (for PL or strongly convex functions), without relying on large batch sizes in each iteration. Furthermore, in the finite-sum case, we develop the SMVR-FS algorithm, which can achieve a complexity of $\mathcal {O}(\sqrt{n}/\epsilon ^{2})$O(n/ε2) for non-convex objectives, $\mathcal {O}(\sqrt{n}/\epsilon \log (1/\epsilon ))$O(n/εlog(1/ε)) for convex functions and $\mathcal {O}(\sqrt{n}/\mu \log (1/\epsilon ))$O(n/μlog(1/ε)) for objectives satisfying the $\mu$μ-PL condition, where $n$...