A proximal point algorithm based on decomposition method for cone constrained multiobjective optimization problems

Jiawei Chen (), Qamrul Hasan Ansari (), Yeong-Cheng Liou () and Jen-Chih Yao ()
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Jiawei Chen: Southwest University
Qamrul Hasan Ansari: Aligarh Muslim University
Yeong-Cheng Liou: Cheng Shiu University
Jen-Chih Yao: China Medical University

Computational Optimization and Applications, 2016, vol. 65, issue 1, No 10, 289-308

Abstract: Abstract By using auxiliary principle technique, a new proximal point algorithm based on decomposition method is suggested for computing a weakly efficient solution of the constrained multiobjective optimization problem (MOP) without assuming the nonemptiness of its solution set. The optimality conditions for (MOP) are derived by the Lagrangian function of its subproblem and corresponding mixed variational inequality. Some basic properties and convergence results of the proposed method are established under some mild assumptions. As an application, we employ the proposed method to solve a split feasibility problem. Finally, numerical results are also presented to illustrate the feasibility of the proposed algorithm.

Keywords: Multiobjective optimization with cone constraints; Mixed variational inequalities; Split feasibility problems; Proximal point algorithm; Auxiliary principle; Decomposition method (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10589-016-9840-2

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