Pessimistic bi-level optimization

We study a variant of the pessimistic bi-level optimization problem, which comprises constraints that must be satis ed for any optimal solution of a subordinate (lower-level) optimization problem. We present conditions that guarantee the existence of optimal solutions in such a problem, and we characterize the computational complexity of various subclasses of the problem. We then focus on problem instances that may lack convexity, but that satisfy a certain independence property. We develop convergent approximations for these instances, and we derive an iterative solution scheme that is reminiscent of the discretization techniques used in semi-in nite programming. We also present a computational study that illustrates the numerical behavior of our algorithm on standard benchmark instances