TY - JOUR
T1 - Constraint Qualifications for Karush–Kuhn–Tucker Conditions in Multiobjective Optimization
AU - Haeser, Gabriel
AU - Ramos, Alberto
N1 - Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - The notion of a normal cone of a given set is paramount in optimization and variational analysis. In this work, we give a definition of a multiobjective normal cone, which is suitable for studying optimality conditions and constraint qualifications for multiobjective optimization problems. A detailed study of the properties of the multiobjective normal cone is conducted. With this tool, we were able to characterize weak and strong Karush–Kuhn–Tucker conditions by means of a Guignard-type constraint qualification. Furthermore, the computation of the multiobjective normal cone under the error bound property is provided. The important statements are illustrated by examples.
AB - The notion of a normal cone of a given set is paramount in optimization and variational analysis. In this work, we give a definition of a multiobjective normal cone, which is suitable for studying optimality conditions and constraint qualifications for multiobjective optimization problems. A detailed study of the properties of the multiobjective normal cone is conducted. With this tool, we were able to characterize weak and strong Karush–Kuhn–Tucker conditions by means of a Guignard-type constraint qualification. Furthermore, the computation of the multiobjective normal cone under the error bound property is provided. The important statements are illustrated by examples.
KW - Constraint qualifications
KW - Multiobjective optimization
KW - Optimality conditions
KW - Regularity
KW - Weak and strong Kuhn–Tucker conditions
UR - https://www.scopus.com/pages/publications/85091731644
U2 - 10.1007/s10957-020-01749-z
DO - 10.1007/s10957-020-01749-z
M3 - Article
AN - SCOPUS:85091731644
SN - 0022-3239
VL - 187
SP - 469
EP - 487
JO - Journal of Optimization Theory and Applications
JF - Journal of Optimization Theory and Applications
IS - 2
ER -