Surrogate duality for vector optimization
作者:
Juan-Enrique Martinez-Legaz,
Ivan Singer,
期刊:
Numerical Functional Analysis and Optimization
(Taylor Available online 1987)
卷期:
Volume 9,
issue 5-6
页码: 547-568
ISSN:0163-0563
年代: 1987
DOI:10.1080/01630568708816247
出版商: Marcel Dekker, Inc.
数据来源: Taylor
摘要:
Using our theorems (of [12]) on separation of convex sets by linear operators, in the sense of the lexi-cographical order on Rn, we prove some theorems of surrogate duality for vector optimization problems with convex constraints (but no regularity assumption), where the surrogate constraint sets are generalized half-spaces and the surrogate multipliers are linear operators, or isomorphisms, or isometries. In the cae of inequality constraints, we prove that the surrogate multipliers can be taken lexicographically non-negative isometries or non-negative (in the usual order) linear isomorphisms.
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