English

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 O(d3)O(d^{3}) to O(dω+d2+a+o(1)+d1+ω(1,1,a)a)O(d^{\omega} + d^{2+a+o(1)} + d^{1+\omega(1,1,a)-a}) for any a[0,1]a \in [0, 1], where ω\omega is the exponent of matrix multiplication. In particular, for the current ω2.371\omega \approx 2.371 [Alman, Duan, Williams, Xu, Xu, Zhou 2024], our running time boils down to O(d2.53)O(d^{2.53}). 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}
}
R2 v1 2026-06-28T18:20:25.079Z