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A New Algorithm for Positive Semidefinite Matrix Completion

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  • Fangfang Xu
  • Peng Pan

Abstract

Positive semidefinite matrix completion (PSDMC) aims to recover positive semidefinite and low-rank matrices from a subset of entries of a matrix. It is widely applicable in many fields, such as statistic analysis and system control. This task can be conducted by solving the nuclear norm regularized linear least squares model with positive semidefinite constraints. We apply the widely used alternating direction method of multipliers to solve the model and get a novel algorithm. The applicability and efficiency of the new algorithm are demonstrated in numerical experiments. Recovery results show that our algorithm is helpful.

Suggested Citation

  • Fangfang Xu & Peng Pan, 2016. "A New Algorithm for Positive Semidefinite Matrix Completion," Journal of Applied Mathematics, Hindawi, vol. 2016, pages 1-5, October.
    • Handle: RePEc:hin:jnljam:1659019
    DOI: 10.1155/2016/1659019
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