  This paper studies the network optimization problem about which a group of
agents cooperates to minimize a global function under practical constraints of
finite bandwidth communication. Particularly, we propose an adaptive
encoding-decoding scheme to handle the constrained communication between
agents. Based on this scheme, the continuous-time quantized distributed
primal-dual (QDPD) algorithm is developed for network optimization problems. We
prove that our algorithms can exactly track an optimal solution to the
corresponding convex global cost function at a linear convergence rate.
Furthermore, we obtain the relation between communication bandwidth and the
convergence rate of QDPD algorithms. Finally, an exponential regression example
is given to illustrate our results.
