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A randomized feasible algorithm for optimization with orthogonal constraints

Author

Listed:
  • Fan Fei

    (Shanghai Jiao Tong University)

  • Yuchen Feng

    (Shanghai Jiao Tong University)

  • Jinyan Fan

    (Shanghai Jiao Tong University)

Abstract

In this paper, we propose a randomized feasible algorithm for optimization over the Stiefel manifold, where only some randomly chosen columns of the variable matrix are updated at each iteration. It is proved that the sequence of Riemannian gradients generated by the algorithm converges to zero with probability one. Numerical results show that the algorithm is efficient, especially for the problems when the matrices involved are sparse.

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  • Fan Fei & Yuchen Feng & Jinyan Fan, 2025. "A randomized feasible algorithm for optimization with orthogonal constraints," Computational Optimization and Applications, Springer, vol. 92(1), pages 1-27, September.
    • Handle: RePEc:spr:coopap:v:92:y:2025:i:1:d:10.1007_s10589-025-00693-w
    DOI: 10.1007/s10589-025-00693-w
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