LAUSR

In this study, we aim to recover the interval hull of the set of feasible cost functions that can make uncertain observations optimal for a class of nonlinear constrained parametric optimization problems when all uncertainty and disturbances acting on observations or modeling are taken bounded but otherwise unknown. Fostering on inverse Karush–Kuhn–Tucker optimality conditions, we first state the solving equations as a constraint satisfaction problem, then show how to derive a safe overapproximation of the feasible solution set combining standard numerical tools and a posteriori validation with guaranteed methods based on interval analysis. The approach is evaluated on two well-tuned numerical examples: A discrete unicycle robot model and a planar elastica model, respectively.