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A sparse optimization problem with hybrid $$L_2{\text {-}}L_p$$ L 2 - L p regularization for application of magnetic resonance brain images

Author

Listed:
  • Xuerui Gao

    (Shanghai University)

  • Yanqin Bai

    (Shanghai University)

  • Qian Li

    (Shanghai University of Engineering Science)

Abstract

Regularization techniques have been proved useful in an enormous variety of sparse optimization problem. In this paper, we introduce a new formulation of regularization with a hybrid $$L_2{\text {-}}L_p~(0

Suggested Citation

  • Xuerui Gao & Yanqin Bai & Qian Li, 2021. "A sparse optimization problem with hybrid $$L_2{\text {-}}L_p$$ L 2 - L p regularization for application of magnetic resonance brain images," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 760-784, November.
    • Handle: RePEc:spr:jcomop:v:42:y:2021:i:4:d:10.1007_s10878-019-00479-x
    DOI: 10.1007/s10878-019-00479-x
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    References listed on IDEAS

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    1. Xiaojun Chen & Weijun Zhou, 2014. "Convergence of the reweighted ℓ 1 minimization algorithm for ℓ 2 –ℓ p minimization," Computational Optimization and Applications, Springer, vol. 59(1), pages 47-61, October.
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