supported by the Taishan Scholar Construction Engineering by Shandong Government the National Natural Science Foundation of China under Grant Nos.61120106011 and 61203029
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 coeff...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.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.71125005,70871108,and 70810107020Outstanding Talents Funds of Organization Department,Beijing Committee of CPC
文摘supported by the Taishan Scholar Construction Engineering by Shandong Government the National Natural Science Foundation of China under Grant Nos.61120106011 and 61203029
基金supported by National Social Science Foundation of China (Grant No. 11BGL053)National Natural Science Foundation of China (Grant Nos. 11101434,10971122 and 11101274)+4 种基金Scientific and Technological Projects of Shandong Province (Grant No. 2009GG10001012)Excellent Young Scientist Foundation of Shandong Province (Grant No. 2010BSE06047)the Doctoral Program of Higher Education of China (Grant No. 20110073120069)Shandong Province Natural Science Foundation (Grant No. ZR2012GQ004)Independent Innovation Foundation of Shandong University (Grant No. 12120083399170)
文摘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.
