Robust solutions of Linear Programming problems contaminated with uncertain data
- Cite this article as:
- Ben-Tal, A. & Nemirovski, A. Math. Program. (2000) 88: 411. doi:10.1007/PL00011380
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Optimal solutions of Linear Programming problems may become severely infeasible if the nominal data is slightly perturbed. We demonstrate this phenomenon by studying 90 LPs from the well-known NETLIB collection. We then apply the Robust Optimization methodology (Ben-Tal and Nemirovski [1–3]; El Ghaoui et al. [5, 6]) to produce “robust” solutions of the above LPs which are in a sense immuned against uncertainty. Surprisingly, for the NETLIB problems these robust solutions nearly lose nothing in optimality.