Constraint qualifications in linear vector semi-infinite optimization
European Journal of Operational Research, 2013•Elsevier
Linear vector semi-infinite optimization deals with the simultaneous minimization of finitely
many linear scalar functions subject to infinitely many linear constraints. This paper provides
characterizations of the weakly efficient, efficient, properly efficient and strongly efficient
points in terms of cones involving the data and Karush–Kuhn–Tucker conditions. The latter
characterizations rely on different local and global constraint qualifications. The global
constraint qualifications are illustrated on a collection of selected applications.
many linear scalar functions subject to infinitely many linear constraints. This paper provides
characterizations of the weakly efficient, efficient, properly efficient and strongly efficient
points in terms of cones involving the data and Karush–Kuhn–Tucker conditions. The latter
characterizations rely on different local and global constraint qualifications. The global
constraint qualifications are illustrated on a collection of selected applications.
Linear vector semi-infinite optimization deals with the simultaneous minimization of finitely many linear scalar functions subject to infinitely many linear constraints. This paper provides characterizations of the weakly efficient, efficient, properly efficient and strongly efficient points in terms of cones involving the data and Karush–Kuhn–Tucker conditions. The latter characterizations rely on different local and global constraint qualifications. The global constraint qualifications are illustrated on a collection of selected applications.
Elsevier
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