Recoverable Robustness and its Application in Linear Programming

Recoverable robustness is a concept in optimization that deals with the capability of a system or a process to withstand disturbances or changes. In linear programming, recoverable robustness can be applied to ensure that the solution remains valid even in the presence of uncertainty or variations in the input data. By using algorithms and techniques to incorporate recoverable robustness into linear programming models, the resulting solutions can be more reliable and effective. This article explores the concept of recoverable robustness in the context of linear programming and its application in various scenarios.