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Multiobjective BFGS method for optimization on Riemannian manifolds

Author

Listed:
  • Shahabeddin Najafi

    (Shahid Beheshti University)

  • Masoud Hajarian

    (Shahid Beheshti University)

Abstract

This paper introduces a Riemannian BFGS method to address multiobjective optimization problems with strongly retraction-convex objective functions. This method is a natural extension of the Euclidean version and produces a sequence of iterates that converges to a Pareto optimal point regardless of the initial point. The main component of our globalization strategy is a generalized Wolfe line search. Numerical experiments demonstrate the superiority and effectiveness of the proposed algorithm.

Suggested Citation

  • Shahabeddin Najafi & Masoud Hajarian, 2024. "Multiobjective BFGS method for optimization on Riemannian manifolds," Computational Optimization and Applications, Springer, vol. 87(2), pages 337-354, March.
    • Handle: RePEc:spr:coopap:v:87:y:2024:i:2:d:10.1007_s10589-023-00522-y
    DOI: 10.1007/s10589-023-00522-y
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    References listed on IDEAS

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    Cited by:

    1. Shahabeddin Najafi & Masoud Hajarian, 2025. "A Globally Convergent Riemannian Memory Gradient Method," Journal of Optimization Theory and Applications, Springer, vol. 206(2), pages 1-17, August.

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