Adaptive sieving: A dimension reduction technique for sparse optimization problems

In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, wherein only a sequence of reduced problems with much smaller sizes need to be solved. We further apply the proposed AS str...

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Bibliographic Details
Published inarXiv.org
Main Authors Yancheng Yuan, Lin, Meixia, Sun, Defeng, Kim-Chuan Toh
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.06.2023
Subjects
Machine learning
Optimization
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Summary:In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, wherein only a sequence of reduced problems with much smaller sizes need to be solved. We further apply the proposed AS strategy to generate solution paths for large-scale sparse optimization problems efficiently. We establish the theoretical guarantees for the proposed AS strategy including its finite termination property. Extensive numerical experiments are presented in this paper to demonstrate the effectiveness and flexibility of the AS strategy to solve large-scale machine learning models.
ISSN:2331-8422