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A Quasi-Newton Method with Wolfe Line Searches for Multiobjective Optimization

Author

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  • L. F. Prudente

    (Universidade Federal de Goiás)

  • D. R. Souza

    (Universidade Federal de Goiás)

Abstract

We propose a BFGS method with Wolfe line searches for unconstrained multiobjective optimization problems. The algorithm is well defined even for general nonconvex problems. Global convergence and R-linear convergence to a Pareto optimal point are established for strongly convex problems. In the local convergence analysis, if the objective functions are locally strongly convex with Lipschitz continuous Hessians, the rate of convergence is Q-superlinear. In this respect, our method exactly mimics the classical BFGS method for single-criterion optimization.

Suggested Citation

  • L. F. Prudente & D. R. Souza, 2022. "A Quasi-Newton Method with Wolfe Line Searches for Multiobjective Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 1107-1140, September.
    • Handle: RePEc:spr:joptap:v:194:y:2022:i:3:d:10.1007_s10957-022-02072-5
    DOI: 10.1007/s10957-022-02072-5
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    References listed on IDEAS

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    1. Kin Keung Lai & Shashi Kant Mishra & Bhagwat Ram, 2020. "On q -Quasi-Newton’s Method for Unconstrained Multiobjective Optimization Problems," Mathematics, MDPI, vol. 8(4), pages 1-14, April.
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    Cited by:

    1. Matteo Lapucci & Pierluigi Mansueto, 2023. "A limited memory Quasi-Newton approach for multi-objective optimization," Computational Optimization and Applications, Springer, vol. 85(1), pages 33-73, May.

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