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Beschreibungen/Notizen
A robust approach to solving linear optimizationproblems with uncertain data was proposed in the early 1970s and has recentlybeen extensively studied and extended. Under this approach, we are willing toaccept a suboptimal solution for the nominal values of the data in order toensure that the solution remains feasible and near optimal when the datachanges. A concern with such an approach is that it might be too conservative.In this paper, we propose an approach that attempts to make this trade-off moreattractive; that is, we investigate ways to decrease what we call the price ofrobustness. In particular, we flexibly adjust the level of conservatism of therobust solutions in terms of probabilistic bounds of constraint violations. Anattractive aspect of our method is that the new robust formulation is also alinear optimization problem. Thus we naturally extend our methods to discreteoptimization problems in a tractable way. We report numerical results for aportfolio optimization problem, a knapsack problem, and a problem from the NetLib library.