The paper by Aharon Ben-Tal and Arkadi Nemirovski explores the robustness of solutions to Linear Programming (LP) problems when the nominal data is slightly perturbed. They demonstrate this phenomenon by studying 90 LPs from the NETLIB collection, showing that even small perturbations can make the nominal optimal solution severely infeasible. To address this issue, they apply the Robust Optimization methodology, which produces "robust" solutions that are immune to uncertainty. Surprisingly, these robust solutions nearly maintain the same optimality as the nominal solutions. The authors define a "reliability index" to quantify the level of infeasibility of the nominal solution under small uncertainties. They then use two robust optimization approaches: one for "unknown-but-bounded" uncertainty and another for "random symmetric" uncertainty. The results show that robust solutions exist and are computationally feasible, with the price of immunization being surprisingly low, often less than 1% in terms of the objective value. Additionally, the paper investigates how close the nominal solution is to reliable solutions. Through experiments, they find that for reasonable levels of uncertainty (0.1%), a small correction can make the nominal solution reliable, with the resulting solution having nearly the same objective value as the best reliable solution. This highlights the practical importance of robust optimization in ensuring the reliability of LP solutions in real-world applications.The paper by Aharon Ben-Tal and Arkadi Nemirovski explores the robustness of solutions to Linear Programming (LP) problems when the nominal data is slightly perturbed. They demonstrate this phenomenon by studying 90 LPs from the NETLIB collection, showing that even small perturbations can make the nominal optimal solution severely infeasible. To address this issue, they apply the Robust Optimization methodology, which produces "robust" solutions that are immune to uncertainty. Surprisingly, these robust solutions nearly maintain the same optimality as the nominal solutions. The authors define a "reliability index" to quantify the level of infeasibility of the nominal solution under small uncertainties. They then use two robust optimization approaches: one for "unknown-but-bounded" uncertainty and another for "random symmetric" uncertainty. The results show that robust solutions exist and are computationally feasible, with the price of immunization being surprisingly low, often less than 1% in terms of the objective value. Additionally, the paper investigates how close the nominal solution is to reliable solutions. Through experiments, they find that for reasonable levels of uncertainty (0.1%), a small correction can make the nominal solution reliable, with the resulting solution having nearly the same objective value as the best reliable solution. This highlights the practical importance of robust optimization in ensuring the reliability of LP solutions in real-world applications.