Abstract: nverse and reverse optimization problems aim to adjust the objective
function of an underlying optimization problem while minimizing the extent of modification. In inverse optimization, the goal is to modify the objective function so that a given feasible solution becomes optimal. In reverse optimization, the goal is to modify the objective function so that the optimum value attains a specified number.
In this talk, we mainly focus on inverse maximum-capacity optimization problems under the bottleneck Hamming distance, the weighted infinity norm and weighted span objectives. Our main contributions include simple, purely combinatorial algorithms that efficiently solve these general problems, assuming that an efficient algorithm is available for the underlying optimization problema.