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A Nonmonotone Trust Region Method for Unconstrained Optimization Problems on Riemannian Manifolds

Author

Listed:
  • Xiaobo Li

    (Southwest Petroleum University)

  • Xianfu Wang

    (University of British Columbia Okanagan)

  • Manish Krishan Lal

    (University of British Columbia Okanagan)

Abstract

We propose a nonmonotone trust region method for unconstrained optimization problems on Riemannian manifolds. Global convergence to the first-order stationary points is proved under some reasonable conditions. We also establish local R-linear, super-linear and quadratic convergence rates. Preliminary experiments show that the algorithm is efficient.

Suggested Citation

  • Xiaobo Li & Xianfu Wang & Manish Krishan Lal, 2021. "A Nonmonotone Trust Region Method for Unconstrained Optimization Problems on Riemannian Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 188(2), pages 547-570, February.
    • Handle: RePEc:spr:joptap:v:188:y:2021:i:2:d:10.1007_s10957-020-01796-6
    DOI: 10.1007/s10957-020-01796-6
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    References listed on IDEAS

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    1. Xiao-bo Li & Nan-jing Huang & Qamrul Hasan Ansari & Jen-Chih Yao, 2019. "Convergence Rate of Descent Method with New Inexact Line-Search on Riemannian Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 830-854, March.
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