PARALLEL COMPUTATION OF LARGE-SCALE NONLINEAR NETWORK PROBLEMS IN THE SOCIAL AND ECONOMIC SCIENCES

In this paper we focus on the parallel computation of large-scale equilibrium and optimization problems arising in the social and economic sciences. In particular, we consider problems that can be visualized and conceptualized as non-linear network flow problems. The underlying network structure is then exploited in the development of parallel decomposition algorithms. We first consider market equilibrium problems, both dynamic and static, that are formulated as variational inequality problems, and for which we propose parallel decomposition algorithms by time period and by commodity, respectively. We then turn to the parallel computation of large-scale constrained matrix problems which are formulated as optimization problems and discuss the results of parallel decomposition by row/column.