A modified sequential quadratic programming method for sparse signal recovery problems

被引：4
|
作者：
Alamdari, Mohammad Saeid [1 ]
Fatemi, Masoud [1 ]
Ghaffari, Aboozar [2 ]
机构：
[1] KN Toosi Univ Technol, Dept Math, Tehran, Iran
[2] Iran Univ Sci & Technol, Dept Elect Engn, Tehran, Iran
来源：
SIGNAL PROCESSING | 2023年 / 207卷
关键词：
Sparse signal recovery; Sequential programming method; SL0; approximation; Non-convex optimization; ALGORITHM; CONVERGENCE; SYSTEM;
D O I：
10.1016/j.sigpro.2023.108955
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We propose a modified sequential quadratic programming method for solving the sparse signal recovery problem. We start by going through the well-known smoothed-l(0) technique and provide a smooth ap-proximation of the objective function. Then, a variant of the sequential quadratic programming method equipped with a new approach for solving subproblems is proposed. We investigate the global con-vergence of the method in detail. In comparison to several well-known algorithms, simulation results demonstrate the promising performance of the proposed method.(c) 2023 Elsevier B.V. All rights reserved.
引用
收藏
页数：10
相关论文
共 50 条
  • [1] The Sequential Quadratic Programming Method
    Fletcher, Roger
    NONLINEAR OPTIMIZATION, 2010, 1989 : 165 - 214
  • [2] A Stabilized Sequential Quadratic Programming Method for Optimization Problems in Function Spaces
    Yamakawa, Yuya
    NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION, 2023, 44 (09) : 867 - 905
  • [3] A sequential quadratically constrained quadratic programming method for unconstrained minimax problems
    Jian, Jin-bao
    Chao, Mian-tao
    JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 2010, 362 (01) : 34 - 45
  • [4] A Sequential Quadratic Programming Method for Constrained Multi-objective Optimization Problems
    Ansary, Md Abu Talhamainuddin
    Panda, Geetanjali
    JOURNAL OF APPLIED MATHEMATICS AND COMPUTING, 2020, 64 (1-2) : 379 - 397
  • [5] Nonuniform Norm Based Method for Sparse Signal Recovery
    Wu, Fei-Yun
    Yang, Kunde
    Tong, Feng
    Hu, Yang
    2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC), 2017,
  • [6] A sequential quadratic penalty method for nonlinear semidefinite programming
    Huang, XX
    Yang, XQ
    Teo, KL
    OPTIMIZATION, 2003, 52 (06) : 715 - 738
  • [7] Stabilized sequential quadratic programming for optimization and a stabilized Newton-type method for variational problems
    Fernandez, Damian
    Solodov, Mikhail
    MATHEMATICAL PROGRAMMING, 2010, 125 (01) : 47 - 73
  • [8] A nonconvex ADMM for a class of sparse inverse semidefinite quadratic programming problems
    Lu, Yue
    Huang, Ming
    Zhang, Yi
    Gu, Jian
    OPTIMIZATION, 2019, 68 (06) : 1075 - 1105
  • [9] A stabilized sequential quadratic semidefinite programming method for degenerate nonlinear semidefinite programs
    Yamakawa, Yuya
    Okuno, Takayuki
    COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2022, 83 (03) : 1027 - 1064
  • [10] An unrolled half-quadratic approach for sparse signal recovery in spectroscopy
    Gharbi, Mouna
    Chouzenoux, Emilie
    Pesquet, Jean-Christophe
    SIGNAL PROCESSING, 2024, 218
12345