arXiv:2609.15679v1 Announce Type: cross
Abstract: This paper studies projection-free algorithms for stochastic constrained multi-level compositional optimization. In this context, the objective funct...
By Wei Jiang, Sifan Yang, Wenhao Yang, Yibo Wang, Yuanyu Wan, Zechao Li, Lijun Zhang
arXiv:2607. 08104v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) is a cornerstone of modern optimization.
By Ryusei Yamada, Naoki Sato, Hideaki Iiduka
arXiv:2510.11676v2 Announce Type: replace-cross
Abstract: We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex fu...
By Chuan He, Bowen Li, Zhaosong Lu
The paper studies stochastic multi‑level optimization where the objective is a nested composition of smooth non‑convex functions. It introduces momentum‑based estimators that track function values at each level, achieving an optimal sample complexity of ≠(ε⁻⁴) for finding an ε‑stationary point without relying on average smoothness assumptions. The authors also present a batch‑free variant using first‑order approximations and clipping, and demonstrate the methods on risk‑averse portfolio optimization and hierarchical tilted empirical risk minimization.
By Wei Jiang, Rui Yan, Sifan Yang, Yuanyu Wan, Lijun Zhang, Zechao Li
arXiv:2406. 13041v3 Announce Type: replace Abstract: Lower-bound analyses for nonconvex strongly-concave minimax optimization problems have shown that stochastic first-order algorithms require at least $\mathcal{O}(\varepsilon^{-4})$ sample complexity to find an $\varepsilon$-stationary point.
By Haoyuan Cai, Sulaiman A. Alghunaim, Ali H. Sayed
arXiv:2609.15723v1 Announce Type: new
Abstract: Traditional variance reduction methods (e.g., SPIDER, SARAH, STORM) have been extensively investigated for improving the convergence rates of stochasti...
By Wei Jiang, Sifan Yang, Yibo Wang, Lijun Zhang, Zechao Li