运筹学学报 ›› 2022, Vol. 26 ›› Issue (1): 43-59.doi: 10.15960/j.cnki.issn.1007-6093.2022.01.003

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非负约束稀疏优化问题的一个等价性条件

吕亚星1, 韩美佳2, 黄子麟2, 朱文兴2*,*()   

  1. 1. 福州大学数学与统计学院, 福建福州 350116
    2. 福州大学离散数学与理论计算机科学研究中心, 福建福州 350116
  • 收稿日期:2021-04-25 出版日期:2022-03-15 发布日期:2022-03-14
  • 通讯作者: 朱文兴 E-mail:wxzhu@fzu.edu.cn
  • 作者简介:朱文兴  E-mail: wxzhu@fzu.edu.cn
  • 基金资助:
    国家自然科学基金(62174033)

An equivalence condition for sparse optimization problem with non-negative constraints

Yaxing LYU1, Meijia HAN2, Zilin HUANG2, Wenxing ZHU2*,*()   

  1. 1. School of Mathematics and Statistics, Fuzhou University, Fuzhou 350116, Fujian, China
    2. Center for Discrete Mathematics and Theoretical Computer Science, FuzhouUniversity, Fuzhou 350116, Fujian, China
  • Received:2021-04-25 Online:2022-03-15 Published:2022-03-14
  • Contact: Wenxing ZHU E-mail:wxzhu@fzu.edu.cn

摘要:

加权l1最小化是稀疏优化的主流方法之一。本文对带非负约束的l0最小化问题与加权l1最小化问题的解之间的关系进行了研究,给出了加权l1最小化问题的约束矩阵和目标函数的系数是"s-权优"的定义,并通过该定义给出了加权l1最小化问题的解是带非负约束的l0最小化问题的解的条件。进一步,本文给出了"s-权优"的充分条件及其具体表示形式,并对其上下界进行了可计算的有效估计。

关键词: 线性规划, 非负稀疏解, 误差分析, 等价性条件

Abstract:

Weighted l1 minimization is one of the mainstream methods for sparse optimization. Since the l0 minimization problem is NP-hard, this paper studies the relationship between solutions of the l0 minimization problem and the weighted l1 minimization problem with non-negative constraints, and gives the definition of "s-weighted goodness" for the constraint matrix and the coefficient of the objective function. Through this definition, a sufficient condition is provided for a solution of the weighted l1 minimization problem to be a solution of the l0 minimization problem with non-negative constraints. Some results and proofs are provided from the perspective of monotonicity and robust stability. Further, this paper gives a sufficient condition for "s-weighted goodness", and shows lower and upper bounds of the sufficient condition, which are easy to check. Moreover, this paper also conducts an error analysis of the solution of the weighted l1 minimization problem.

Key words: linear programming, nonnegative sparse solution, error analysis, equivalence condition

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