Scholtes relaxation method for pessimistic bilevel optimization

Abstract The Scholtes relaxation has appeared to be one of the simplest and most efficient ways to solve the optimistic bilevel optimization problem in its Karush-Kuhn-Tucker (KKT) reformulation or the corresponding more general mathematical program with complementarity constraints (MPCC). Inspired by such a success, this paper studies the potential of the Scholtes relaxation in the context of the pessimistic bilevel optimization problem. To proceed, we consider a pessimistic bilevel optimization problem, where all the functions involved are at least continuously differentiable. Then assuming that the lower-level problem is convex, the KKT reformulation of the problem is considered under the Slater constraint qualification. Based on this KKT reformulation, we introduce the corresponding version of the Scholtes relaxation algorithm. We then construct theoretical results ensuring that a sequence of global/local optimal solutions (resp. stationarity points) of the aforementioned Scholtes relaxation converges to a global/local optimal solution (resp. stationarity point) of the KKT reformulation of the pessimistic bilevel optimization. The results are accompanied by technical results ensuring that the Scholtes relaxation algorithm is well-defined or that the corresponding parametric optimization problem can easily be solved. Furthermore, we perform some numerical experiments to assess the performance of the Scholtes relaxation algorithm using various pessimistic bilevel programs. In particular, we study the effectiveness of the algorithm in obtaining solutions that can satisfy the corresponding C-stationarity concept. Mathematics Subject Classification (2000) 90C26 · 90C31 · 90C33 · 90C46