A Nonmonotone Projected Gradient Method for Multiobjective Problems on Convex Sets

被引:0
作者
Gabrie Aníbal Carrizo
Nadia Soledad Fazzio
María Laura Schuverdt
机构
[1] National University of the South,Department of Mathematics
[2] University of La Plata,Department of Mathematics
来源
Journal of the Operations Research Society of China | 2024年 / 12卷
关键词
Multiobjective optimization; Projected gradient methods; Nonmonotone line search; Global convergence; 90C29; 49M37; 65K05;
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学科分类号
摘要
In this work we consider an extension of the classical scalar-valued projected gradient method for multiobjective problems on convex sets. As in Fazzio et al. (Optim Lett 13:1365–1379, 2019) a parameter which controls the step length is considered and an updating rule based on the spectral gradient method from the scalar case is proposed. In the present paper, we consider an extension of the traditional nonmonotone approach of Grippo et al. (SIAM J Numer Anal 23:707–716, 1986) based on the maximum of some previous function values as suggested in Mita et al. (J Glob Optim 75:539–559, 2019) for unconstrained multiobjective optimization problems. We prove the accumulation points of sequences generated by the proposed algorithm, if they exist, are stationary points of the original problem. Numerical experiments are reported.
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页码:410 / 427
页数:17
相关论文
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