Abstract
Linear programming models have been widely used in input-output analysis for analyzing the interdependence of industries in economics and in environmental science. In these applications, some of the entries of the coefficient matrix cannot be measured physically or there exists sampling errors. However, the coefficient matrix can often be low-rank. We characterize the robust counterpart of these types of linear programming problems with uncertainty set described by the nuclear norm. Simulations for the input-output analysis show that the new paradigm can be helpful.
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