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Robustness has become one of several paradigms used to address data uncertainty in optimization. While the input data may not be known perfectly, it assumes that each input comes from some known bounded region or universe of legal inputs. Robust optimization then seeks an optimal solution that is valid for all possible inputs in the universe; robustness is thus a worst-case approach for coping with uncertainty. The concept was initially developed in continuous optimization where a large class of robust linear programs was shown to be solvable via conic programming. Robust optimization has become increasingly popular in the discrete optimization community. One of the most widely studied examples arises in the context of network design.

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Format:PDFSize:190 KB
Date:Dec 2007
Pages:15

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