# New insights in smoothness and strong convexity with improved convergence of gradient descent

arXiv: https://arxiv.org/abs/2110.15470 · HTML: https://mlsanity.com/paper/2110.15470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2021.

## Built on by (most cited first, 1 of 1)

- [Gradient Correction beyond Gradient Descent](https://mlsanity.com/paper/2203.08345/) (2022)

## Abstract (arXiv)

The starting assumptions to study the convergence and complexity of gradient-type methods may be the smoothness (also called Lipschitz continuity of gradient) and the strong convexity. In this note, we revisit these two basic properties from a new perspective that motivates their definitions and equivalent characterizations, along with an improved linear convergence of the gradient descent method.