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Optimality Conditions via Exact Penalty Functions

Authors: Meng, K; Yang, XQ;

Optimality Conditions via Exact Penalty Functions

Abstract

In this paper, we study KKT optimality conditions for constrained nonlinear programming problems and strong and Mordukhovich stationarities for mathematical programs with complementarity constraints using $l_p$ penalty functions, with $0\leq p\leq1$. We introduce some optimality indication sets by using contingent derivatives of penalty function terms. Some characterizations of optimality indication sets are obtained by virtue of the original problem data. We show that the KKT optimality condition holds at a feasible point if this point is a local minimizer of some $l_p$ penalty function with $p$ belonging to the optimality indication set. Our result on constrained nonlinear programming includes some existing results from the literature as special cases.

Countries
China (People's Republic of), Hong Kong
Keywords

Exact penalty function, KKT optimality condition, Mathematical programs with complementarity constraints, Strong stationarity, Mordukhovich stationarity, Nonlinear programming problem

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Average
Top 10%
Average
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