# Last Iterate Convergence of AdaGrad-Norm for Convex Non-Smooth Optimization

arXiv: https://arxiv.org/abs/2604.10728 · HTML: https://mlsanity.com/paper/2604.10728/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [Dangerous Liaisons of Convex Learning and Non-Affine Aggregation](https://mlsanity.com/paper/2606.28123/) (2026)
- [Revisiting AdaGrad in Stochastic Convex Optimization: Last Iterates, High Probability, and Lower Bounds](https://mlsanity.com/paper/2609.32729/) (2026)
- [Last-Iterate Convergence Rate of Normalized Gradient Descent under H"older Smoothness](https://mlsanity.com/paper/2610.06070/) (2026)

## Abstract (arXiv)

We study the convergence of the last iterate (i.e., the (N+1)-th iterate) of the AdaGrad method. Although AdaGrad -- an adaptive subgradient method -- underpins a wide class of algorithms, most existing convergence analyses focus on averaged (or best) iterates. We derive worst-case upper bounds on the suboptimality of the final point and show that, with an optimally tuned stepsize parameter, the last iterate converges at the rate O(1/N1/4). We complement this guarantee with matching lower-bound constructions, proving that this rate is tight and that AdaGrad's last-iterate rate is strictly worse than the classical O(1/N1/2) rate for its averaged iterate. Technically, our analysis introduces an exponent parameter that captures the growth of the cumulative squared subgradients; combined with the last-iterate inequality of Zamani and Glineur (2025), this reduces the problem to bounding a particular series.