  In this paper, we focus on an aggregative optimization problem under the
communication bottleneck. The aggregative optimization is to minimize the sum
of local cost functions. Each cost function depends on not only local state
variables but also the sum of functions of global state variables. The goal is
to solve the aggregative optimization problem through distributed computation
and local efficient communication over a network of agents without a central
coordinator. Using the variable tracking method to seek the global state
variables and the quantization scheme to reduce the communication cost spent in
the optimization process, we develop a novel distributed quantized algorithm,
called D-QAGT, to track the optimal variables with finite bits communication.
Although quantization may lose transmitting information, our algorithm can
still achieve the exact optimal solution with a linear convergence rate.
Simulation experiments on an optimal placement problem is carried out to verify
the correctness of the theoretical results.
