# Stochastic Gradient Descent Works Really Well for Stress Minimization

arXiv: https://arxiv.org/abs/2008.10376 · HTML: https://mlsanity.com/paper/2008.10376/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

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

- [Multidimensional Scaling: Approximation and Complexity](https://mlsanity.com/paper/2109.11505/) (2021)
- [Balancing between the Local and Global Structures (LGS) in Graph Embedding](https://mlsanity.com/paper/2308.16403/) (2023)
- [Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization](https://mlsanity.com/paper/2605.00641/) (2026)

## Abstract (arXiv)

Stress minimization is among the best studied force-directed graph layout methods because it reliably yields high-quality layouts. It thus comes as a surprise that a novel approach based on stochastic gradient descent (Zheng, Pawar and Goodman, TVCG 2019) is claimed to improve on state-of-the-art approaches based on majorization. We present experimental evidence that the new approach does not actually yield better layouts, but that it is still to be preferred because it is simpler and robust against poor initialization.