2 papers
math.OC2026
Last Iterate Convergence of AdaGrad-Norm for Convex Non-Smooth Optimization
Margarita Preobrazhenskaia, Makar Sidorov, Igor Preobrazhenskii +1
We study the convergence of the last iterate (i.e., the -th iterate) of the AdaGrad method. Although AdaGrad -- an adaptive subgradient method -- underpins a wide class of a…
math.OC2025
Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under -Smoothness
Aleksandr Lobanov, Alexander Gasnikov, Eduard Gorbunov +1
The gradient descent (GD) method -- is a fundamental and likely the most popular optimization algorithm in machine learning (ML), with a history traced back to a paper in 1847 (Cau…