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Two adaptive scaled gradient projection methods for Stiefel manifold constrained optimization

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Abstract

This article is concerned with the problem of minimizing a smooth function over the Stiefel manifold. In order to address this problem, we introduce two adaptive scaled gradient projection methods that incorporate scaling matrices that depend on the step-size and a parameter that controls the search direction. These iterative algorithms use a projection operator based on the QR factorization to preserve the feasibility in each iteration. However, for some particular cases, the proposals do not require the use of any projection operator. In addition, we consider a Barzilai and Borwein-like step-size combined with the Zhang–Hager nonmonotone line-search technique in order to accelerate the convergence of the proposed procedures. We proved the global convergence for these schemes, and we evaluate their effectiveness and efficiency through an extensive computational study, comparing our approaches with other state-of-the-art gradient-type algorithms.

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Notes

  1. The OptStiefel code is available in https://www.math.ucla.edu/~wotaoyin/papers/feasible_method_matrix_manifold.html.

  2. The Riemannian conjugate gradient methods Algor.1a, Algor.1b, and Algor.1b+ZH can be downloaded from http://www.optimization-online.org/DB_HTML/2016/09/5617.html.

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Acknowledgements

The second author wants to thank the Federal University of Santa Catarina–Brazil and remarks that his contribution to the present article was predominantly carried out at this institution.

Funding

This research was supported in part by Conacyt, Mexico (258033 research grant and H.O.L. PhD. studies scholarship).

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Authors and Affiliations

  1. Mathematics Research Center, CIMAT A.C. Guanajuato, Guanajuato, Mexico

    Harry Oviedo & Oscar Dalmau

  2. Universidade Federal de Santa Catarina, Campus Blumenau, Blumenau, Brazil

    Hugo Lara

Authors
  1. Harry Oviedo
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  2. Oscar Dalmau
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  3. Hugo Lara
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Correspondence to Oscar Dalmau.

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Oviedo, H., Dalmau, O. & Lara, H. Two adaptive scaled gradient projection methods for Stiefel manifold constrained optimization. Numer Algor 87, 1107–1127 (2021). https://doi.org/10.1007/s11075-020-01001-9

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  • DOI: https://doi.org/10.1007/s11075-020-01001-9

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