arXiv:2610. 12328v1 Announce Type: new Abstract: We consider stochastic nonsmooth nonconvex composite optimization, which includes several important problems such as constrained optimization and the regularized training of neural networks.
By Mingyi Li, Taira Tsuchiya, Kenji Yamanishi
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:2504. 09409v2 Announce Type: replace-cross Abstract: In this paper, we study nonconvex constrained stochastic zeroth-order optimization problems, for which we have access to exact information of constraints and noisy function values of the objective.
By Qiankun Shi, Han Yuan, Xiao Wang, Hao Wang
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:2608. 05460v1 Announce Type: cross Abstract: This work introduces a proximal stochastic subgradient method for minimizing the sum of an expected cost, whose integrand is potentially nonsmooth and nonconvex, and a lower semicontinuous, prox-bounded function.
By Felipe Atenas, Alejandro Jofr\'e, Pedro P\'erez-Aros, David Torregrosa-Bel\'en
arXiv:2609.08380v1 Announce Type: cross
Abstract: We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone vari...
By Ahmet Alacaoglu