Nonmonotone gradient methods for vector optimization with a portfolio optimization application

Shaojian Qu, Ying Ji, Jianlin Jiang and Qingpu Zhang

European Journal of Operational Research, 2017, vol. 263, issue 2, 356-366

Abstract: This paper proposes two nonmonotone gradient algorithms for a class of vector optimization problems with a C−convex objective function. We establish both the global and local convergence results for the new algorithms. We then apply the new algorithms to a portfolio optimization problem under multi-criteria considerations.

Keywords: (S) Multiple objective programming; Nonmonotone gradient algorithms; Pareto optimum; Convergence; Portfolio optimization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:263:y:2017:i:2:p:356-366

DOI: 10.1016/j.ejor.2017.05.027

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