Dual conditions characterizing optimality for convex multi-objective programs

被引：12
|
作者：
Glover, BM [1 ]
Jeyakumar, V
Rubinov, AM
机构：
[1] Curtin Univ Technol, Res & Dev, Bentley, WA 6102, Australia
[2] Univ New S Wales, Sch Math, Sydney, NSW 2052, Australia
[3] Univ Ballarat, Sch Informat Technol & Math Sci, Ballarat, Vic, Australia
来源：
MATHEMATICAL PROGRAMMING | 1999年 / 84卷 / 01期
关键词：
Pareto minimum; convex programming; epsilon-subdifferentials; multi-objective optimization;
D O I：
10.1007/s10107980013a
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Asymptotic necessary and sufficient conditions for a point to be a Pareto minimum, and weak minimum (proper minimum) for a convex multi-objective program are given without a regularity condition. It is further shown that, in the cases of weak minimum and single objective function, the asymptotic dual conditions reduce to nonasymptotic optimality conditions under Slater's constraint qualification. The results are applied to multi-objective quadratic and linar programming problems. Numerical examples are given to illustrate the nature of the conditions.
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页码：201 / 217
页数：17
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