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An Incremental Subgradient Method on Riemannian Manifolds

Author

Listed:
  • Peng Zhang

    (Harbin Institute of Technology)

  • Gejun Bao

    (Harbin Institute of Technology)

Abstract

In this paper, we propose and analyze an incremental subgradient method with a diminishing stepsize rule for a convex optimization problem on a Riemannian manifold, where the object function consisted of the sum of a large number of component functions. This type of function is useful in lots of fields. We establish an important inequality about the sequence generated by the method, provided the sectional curvature of the manifold is nonnegative. Using the inequality, we prove Proposition 3.1, and then we obtain some convergence results of the incremental subgradient method.

Suggested Citation

  • Peng Zhang & Gejun Bao, 2018. "An Incremental Subgradient Method on Riemannian Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 176(3), pages 711-727, March.
    • Handle: RePEc:spr:joptap:v:176:y:2018:i:3:d:10.1007_s10957-018-1224-6
    DOI: 10.1007/s10957-018-1224-6
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    References listed on IDEAS

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    3. Glaydston C. Bento & Jefferson G. Melo, 2012. "Subgradient Method for Convex Feasibility on Riemannian Manifolds," Journal of Optimization Theory and Applications, Springer, vol. 152(3), pages 773-785, March.
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