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Conditional gradient method for multiobjective optimization

Author

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  • P. B. Assunção

    (Universidade Federal de Goiás)

  • O. P. Ferreira

    (Universidade Federal de Goiás)

  • L. F. Prudente

    (Universidade Federal de Goiás)

Abstract

We analyze the conditional gradient method, also known as Frank–Wolfe method, for constrained multiobjective optimization. The constraint set is assumed to be convex and compact, and the objectives functions are assumed to be continuously differentiable. The method is considered with different strategies for obtaining the step sizes. Asymptotic convergence properties and iteration-complexity bounds with and without convexity assumptions on the objective functions are stablished. Numerical experiments are provided to illustrate the effectiveness of the method and certify the obtained theoretical results.

Suggested Citation

  • P. B. Assunção & O. P. Ferreira & L. F. Prudente, 2021. "Conditional gradient method for multiobjective optimization," Computational Optimization and Applications, Springer, vol. 78(3), pages 741-768, April.
    • Handle: RePEc:spr:coopap:v:78:y:2021:i:3:d:10.1007_s10589-020-00260-5
    DOI: 10.1007/s10589-020-00260-5
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    References listed on IDEAS

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    Cited by:

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    2. Gonçalves, M.L.N. & Lima, F.S. & Prudente, L.F., 2022. "A study of Liu-Storey conjugate gradient methods for vector optimization," Applied Mathematics and Computation, Elsevier, vol. 425(C).

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