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New regularization method and iteratively reweighted algorithm for sparse vector recovery

机译:稀疏向量恢复的新正则化方法和迭代加权算法

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摘要

Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.

著录项

  • 来源
    《应用数学和力学(英文版)》 |2020年第1期|157-172|共16页
  • 作者

    Wei ZHU; Hui ZHANG; Lizhi CHENG;

  • 作者单位

    Post-doctoral Research Station of Statistics School of Mathematics and Computational Science Xiangtan University Xiangtan 411105 Hunan Province China;

    Department of Mathematics National University of Defense Technology Changsha 410073 China;

  • 收录信息 中国科学引文数据库(CSCD);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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