A Tighter Complexity Analysis of SparseGPT
Data Structures and Algorithms
2024-10-21 v2 Artificial Intelligence
Computation and Language
Machine Learning
Abstract
In this work, we improved the analysis of the running time of SparseGPT [Frantar, Alistarh ICML 2023] from to for any , where is the exponent of matrix multiplication. In particular, for the current [Alman, Duan, Williams, Xu, Xu, Zhou 2024], our running time boils down to . This running time is due to the analysis of the lazy update behavior in iterative maintenance problems such as [Deng, Song, Weinstein 2022; Brand, Song, Zhou ICML 2024].
Cite
@article{arxiv.2408.12151,
title = {A Tighter Complexity Analysis of SparseGPT},
author = {Xiaoyu Li and Yingyu Liang and Zhenmei Shi and Zhao Song},
journal= {arXiv preprint arXiv:2408.12151},
year = {2024}
}