Robust optimization is not restricted to linear programming. Many results are available for robust counterparts of other convex optimization problems with various types of uncertainty sets.

1.3 What we consider With these examples in mind, we arrive at three major questions for robust optimization:

Part I, perhaps with chapter 4 skipped, can be used as a stand-alone graduate- level textbook on Robust Linear Programming, or as a base of a semester-long graduate course on Robust …

This paper considers Robust Optimization (RO), a more recent approach to optimization under uncertainty, in which the uncertainty model is not stochastic, but rather deterministic and set …

Distributionally robust optimization : is a mix between robust and stochastic optimization consists in solving a stochastic optimization problem where the law is chosen in a robust way is a fast …

Robust optimization is an important subfield of optimization that deals with uncertainty in the data.

This paper considers Robust Optimization (RO), a more recent approach to optimization under uncertainty, in which the uncertainty model is not stochastic, but rather deterministic and set …