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 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.

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Metadaten
Author:Yancheng Yuan, Meixia Lin, Defeng Sun, Kim-Chuan Toh
DOI:https://doi.org/10.1007/s12532-025-00282-2
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2025
Volume:17
Issue:3
Page Number:32
First Page:585
Last Page:616
Mathematical Programming Computation :MPC 2025 - Issue 3
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