Generic property of the partial calmness condition for bilevel programming problems
Rongzhu Ke, Wei Yao, Jane J. Ye, and Jin Zhang

TL;DR
This paper investigates the partial calmness condition in bilevel programming, establishing its generic nature under certain conditions and providing optimality criteria without extra qualifications, with implications for economics.
Contribution
It introduces a sufficient partial error bound condition guaranteeing partial calmness and proves its genericity for important economic applications.
Findings
Partial error bound condition is generic for combined programs.
Partial calmness is not a stringent assumption in key settings.
Derived optimality conditions match existing implicit forms.
Abstract
The partial calmness for the bilevel programming problem (BLPP) is an important condition which ensures that a local optimal solution of BLPP is a local optimal solution of a partially penalized problem where the lower level optimality constraint is moved to the objective function and hence a weaker constraint qualification can be applied. In this paper we propose a sufficient condition in the form of a partial error bound condition which guarantees the partial calmness condition. We analyse the partial calmness for the combined program based on the Bouligand (B-) and the Fritz John (FJ) stationary conditions from a generic point of view. Our main result states that the partial error bound condition for the combined programs based on B and FJ conditions are generic for an important setting with applications in economics and hence the partial calmness for the combined program is not a…
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Taxonomy
TopicsOptimization and Variational Analysis
