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Support Recovery of Greedy Block Coordinate Descent Using the Near Orthogonality Property

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  • Haifeng Li

Abstract

In this paper, using the near orthogonal property, we analyze the performance of greedy block coordinate descent (GBCD) algorithm when both the measurements and the measurement matrix are perturbed by some errors. An improved sufficient condition is presented to guarantee that the support of the sparse matrix is recovered exactly. A counterexample is provided to show that GBCD fails. It improves the existing result. By experiments, we also point out that GBCD is robust under these perturbations.

Suggested Citation

  • Haifeng Li, 2017. "Support Recovery of Greedy Block Coordinate Descent Using the Near Orthogonality Property," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-7, April.
    • Handle: RePEc:hin:jnlmpe:4903791
    DOI: 10.1155/2017/4903791
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