A Splitting-based Iterative Method for Sparse Reconstruction

  • Liquan Kang
  • Ying Chen
  • Zefeng Yu
  • Heng Wu
  • Zijun Zheng
  • Shanzhou Niu
Keywords: Compressed sensing, Sparse reconstruction, ℓ1-norm regularized minimization, Variable splitting, Convergence


In this paper, we study a ℓ1-norm regularized minimization method for sparse solution recovery in compressed sensing and X-ray CT image reconstruction. In the proposed method, an alternating minimization algorithm is employed to solve the involved ℓ1-norm regularized minimization problem. Under some suitable conditions, the proposed algorithm is shown to be globally convergent. Numerical results indicate that the presented method is effective and promising.


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How to Cite
Kang, L., Chen, Y., Yu, Z., Wu, H., Zheng, Z., & Niu, S. (2016). A Splitting-based Iterative Method for Sparse Reconstruction. Statistics, Optimization & Information Computing, 4(1), 57-67. https://doi.org/10.19139/soic.v4i1.204
Research Articles