Robust Optimization with Xpress - Usage guidelines and examples

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This whitepaper gives an introduction to formulating and solving Robust Optimization problems with FICOTM

Xpress.

Robust optimization is a modelling framework that can be used to model optimization problems under uncertainty

in the input data. The uncertainty is assumed to be defined by a set of values that the uncertain data can

take; this uncertainty set can be described by linear constraints, convex quadratic constraints, or as a discrete set

of vectors of uncertain data. The solutions generated by a robust optimization model are feasible irrespective

of the actual value that the uncertain data will take.

The introductory part explains the general concepts of Robust Optimization and which types of formulations

are available with Xpress. The remainder of this document is formed by a collection of example problems

showing typical uses of Robust Optimization in practice with a discussion of the problem implementation with

Xpress-Mosel.

The examples presented in this whitepaper are included in the examples/robust/Mosel directory of the

Xpress installation

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