Incremental Gauss--Newton Methods with Superlinear Convergence Rates
Optimization and Control
2024-07-04 v1 Machine Learning
Abstract
This paper addresses the challenge of solving large-scale nonlinear equations with H\"older continuous Jacobians. We introduce a novel Incremental Gauss--Newton (IGN) method within explicit superlinear convergence rate, which outperforms existing methods that only achieve linear convergence rate. In particular, we formulate our problem by the nonlinear least squares with finite-sum structure, and our method incrementally iterates with the information of one component in each round. We also provide a mini-batch extension to our IGN method that obtains an even faster superlinear convergence rate. Furthermore, we conduct numerical experiments to show the advantages of the proposed methods.
Cite
@article{arxiv.2407.03195,
title = {Incremental Gauss--Newton Methods with Superlinear Convergence Rates},
author = {Zhiling Zhou and Zhuanghua Liu and Chengchang Liu and Luo Luo},
journal= {arXiv preprint arXiv:2407.03195},
year = {2024}
}
Comments
37 pages, 9 figures