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Sequential characterizations of approximate solutions in convex vector optimization problems with set-valued maps

Nithirat Sisarat (), Rabian Wangkeeree () and Tamaki Tanaka ()
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Nithirat Sisarat: Naresuan University
Rabian Wangkeeree: Naresuan University
Tamaki Tanaka: Niigata University

Journal of Global Optimization, 2020, vol. 77, issue 2, No 3, 273-287

Abstract: Abstract This paper deals with a convex vector optimization problem with set-valued maps. In the absence of constraint qualifications, it provides, by the scalarization theorem, sequential Lagrange multiplier conditions characterizing approximate weak Pareto optimal solutions for the problem in terms of the approximate subdifferentials of the marginal function associated with corresponding set-valued maps. The paper shows also that this result yields the approximate Lagrange multiplier condition for the problem under a new constraint qualification which is weaker than the Slater-type constraint qualification. Illustrative examples are also provided to discuss the significance of the sequential conditions.

Keywords: Convex vector optimization problems with set-valued maps; Sequential Lagrange multiplier conditions; Constraint qualifications; Scalarizations; Approximate weak Pareto optimal solutions; 90C26; 90C29; 90C46; 90C48 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10898-019-00864-0

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