A Resolution of the SS--RS--GD Inequalities
Optimization and Control
2026-06-16 v1 Machine Learning
Machine Learning
Abstract
Yun, Sra, and Jadbabaie (COLT 2021, open question) conjectured the SS--RS--GD inequalities: for well-conditioned symmetric matrices , the operators , , and that encode the expected iterate of single-shuffle SGD, random-reshuffle SGD, and gradient descent on a quadratic finite sum should satisfy The conjecture is resolved, SS-RS inequality fails. Already for , , and , we exhibit explicit PSD matrices whose condition number is arbitrarily close to , yet . RS-GD inequality holds. For every symmetric with , one has . The proof was found via GPT-5.5 Pro extended prompted by the author.
Keywords
Cite
@article{arxiv.2607.22620,
title = {A Resolution of the SS--RS--GD Inequalities},
author = {Binghui Peng},
journal= {arXiv preprint arXiv:2607.22620},
year = {2026}
}