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Journal of Applied Mathematics and Computing

Erschienen in:

01.10.2021 | Original Research

Combined gradient methods for multiobjective optimization

verfasst von: Peng Wang, Detong Zhu

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 4/2022

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Abstract

In this paper, the combined gradient methods are designed to solve multiobjective optimization problems. According to the special structure of the problem, we only use the gradient information of each objective function and combine each gradient by combining parameters to obtain the search direction of the problem. The Hessian matrix of each objective function is avoided in the methods. Under the assumption that the gradient of objective function is linearly independent, we prove that the methods can always produce a subsequence that converges to the local Pareto point of the problem, and analysis its worst-case iteration complexity. The numerical results are reported for showing the effectiveness of the algorithm.

Vorheriger Artikel The Drazin inverse of anti-triangular block matrices

Nächster Artikel Fractional integro-differential sliding mode control of a class of distributed-order nonlinear systems

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Titel: Combined gradient methods for multiobjective optimization
verfasst von: Peng Wang
Detong Zhu
Publikationsdatum: 01.10.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Applied Mathematics and Computing / Ausgabe 4/2022
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-021-01636-4

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