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Robust solutions of Linear Programming problems contaminated with uncertain data

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Abstract.

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.

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Received: July 1999 / Accepted: May 2000¶Published online July 20, 2000

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Ben-Tal, A., Nemirovski, A. Robust solutions of Linear Programming problems contaminated with uncertain data. Math. Program. 88, 411–424 (2000). https://doi.org/10.1007/PL00011380

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  • DOI: https://doi.org/10.1007/PL00011380

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