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Alternating Direction Method of Multipliers for Separable Convex Optimization of Real Functions in Complex Variables

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  • Lu Li
  • Xingyu Wang
  • Guoqiang Wang

Abstract

The alternating direction method of multipliers (ADMM) has been widely explored due to its broad applications, and its convergence has been gotten in the real field. In this paper, an ADMM is presented for separable convex optimization of real functions in complex variables. First, the convergence of the proposed method in the complex domain is established by using the Wirtinger Calculus technique. Second, the basis pursuit (BP) algorithm is given in the form of ADMM in which the projection algorithm and the soft thresholding formula are generalized from the real case. The numerical simulations on the reconstruction of electroencephalogram (EEG) signal are provided to show that our new ADMM has better behavior than the classic ADMM for solving separable convex optimization of real functions in complex variables.

Suggested Citation

  • Lu Li & Xingyu Wang & Guoqiang Wang, 2015. "Alternating Direction Method of Multipliers for Separable Convex Optimization of Real Functions in Complex Variables," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, December.
    • Handle: RePEc:hin:jnlmpe:104531
    DOI: 10.1155/2015/104531
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    Cited by:

    1. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

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