The paper introduces a parallel architecture for stochastic gradient methods that adaptively selects the number of iterations. An algorithm A(x₀, y) takes an initial point and a step limit y, and p processors search for an appropriate iteration count T using a prescribed function h. The framework guarantees a (p, αₚ)-approximation, meaning for any T ≥ T₀ there exists a processor and stage where the cumulative iterations lie within a factor αₚ of T, and the authors prove tight lower bounds for αₚ while presenting simple arithmetic stochastic gradient methods that use only divisions by powers of two.
By Bin Fu
arXiv:2502.21099v3 Announce Type: replace-cross
Abstract: This paper proposes {\sf AEPG-SPIDER}, an Adaptive Extrapolated Proximal Gradient (AEPG) method with variance reduction for minimizing compos...
By Ganzhao Yuan
arXiv:2505.20817v3 Announce Type: replace-cross
Abstract: Gradient clipping is widely used in language-model training to control heavy-tailed gradient noise and can improve convergence guarantees ove...
By Taha El Bakkali El Kadi, Savelii Chezhegov, Aleksandr Beznosikov, Samuel Horv\'ath, Eduard Gorbunov
arXiv:2603. 10485v3 Announce Type: replace-cross Abstract: In this work, we study the convergence properties of the Dual Space Preconditioned Gradient Descent, encompassing optimizers such as Normalized Gradient Descent and Gradient Clipping.
By Reza Ghane, Danil Akhtiamov, Babak Hassibi
arXiv:2605. 18694v2 Announce Type: replace-cross Abstract: Many tasks in modern machine learning are observed to involve heavy-tailed gradient noise during the optimization process.
By Zijian Liu
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