Optimization of Buffer Networks via DC Programming

This brief is concerned with the
$H^{2}$
and
$H^{\infty }$
norm-constrained optimization problems of dynamic buffer networks. The extended network model is introduced first, wherein the weights of all edges can be tuned independently. Because of the emerging nonconvexity of the extended model, previous results of positive linear systems failed to address this situation. By resorting to the log–log convexity of a class of nonlinear functions called posynomials, the optimization problems can be reduced to differential convex programming problems. The proposed framework is illustrated for large-scale networks.