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Interior-Point Gradient Method for Large-Scale Totally Nonnegative Least Squares Problems

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Abstract

We study an interior-point gradient method for solving a class of so-called totally nonnegative least-squares problems. At each iteration, the method decreases the residual norm along a diagonally-scaled negative gradient direction with a special scaling. We establish the global convergence of the method and present some numerical examples to compare the proposed method with a few similar methods including the affine scaling method.

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Authors and Affiliations

  1. Graduate Student, Department of Computational and Applied Mathematics, Rice University, Houston, Texas

    M. Merritt

  2. Professor, Department of Computational and Applied Mathematics, Rice University, Houston, Texas, USA

    Y. Zhang

Authors
  1. M. Merritt
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  2. Y. Zhang
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Additional information

This author was supported in part by DOE/LANL Contract 03891-99-23

This author was supported in part by NSF Grant DMS-0442065

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Merritt, M., Zhang, Y. Interior-Point Gradient Method for Large-Scale Totally Nonnegative Least Squares Problems. J Optim Theory Appl 126, 191–202 (2005). https://doi.org/10.1007/s10957-005-2668-z

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  • DOI: https://doi.org/10.1007/s10957-005-2668-z

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