Calmness and exact penalization in vector optimization with cone constraints

X. X. Huang; K. L. Teo; Xiaoqi Yang

doi:10.1007/s10589-006-6441-5

Calmness and exact penalization in vector optimization with cone constraints

X. X. Huang, K. L. Teo, Xiaoqi Yang

Research output: Journal article publication › Journal article › Academic research › peer-review

17 Citations (Scopus)

Abstract

In this paper, a (local) calmness condition of order α is introduced for a general vector optimization problem with cone constraints in infinite dimensional spaces. It is shown that the (local) calmness is equivalent to the (local) exact penalization of a vector-valued penalty function for the constrained vector optimization problem. Several necessary and sufficient conditions for the local calmness of order α are established. Finally, it is shown that the local calmness of order 1 implies the existence of normal Lagrange multipliers.

Original language	English
Pages (from-to)	47-67
Number of pages	21
Journal	Computational Optimization and Applications
Volume	35
Issue number	1
DOIs	https://doi.org/10.1007/s10589-006-6441-5
Publication status	Published - 1 Sept 2006

Keywords

Calmness
Efficient solution
Exact penalization
Normal Lagrange multiplier
Vector optimization with cone constraints
Weakly efficient solution

ASJC Scopus subject areas

Applied Mathematics
Control and Optimization
Management Science and Operations Research
Computational Mathematics

More information

10.1007/s10589-006-6441-5

Cite this

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KW - Efficient solution

KW - Exact penalization

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KW - Vector optimization with cone constraints

KW - Weakly efficient solution

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Calmness and exact penalization in vector optimization with cone constraints

Abstract

Keywords

ASJC Scopus subject areas

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