Balancing large social accounting matrices with nonlinear network programming

We formulate the problem of optimally adjusting the components of a large matrix to satisfy consistency requirements as a nonlinear network optimization model. An efficient network optimization algorithm—GENOS—is incorporated in a user friendly modeling system—GAMS. The resulting software is used for balancing large Social Accounting Matrices (SAM). We assemble a library of SAM models from developing countries and report computational results.