  Communication compression techniques are of growing interests in studying the
decentralized optimization problem under limited communication, where the
global objective is to minimize the average of local cost functions over
networked agents using only local computation and peer-to-peer communication.
In this paper, we propose a novel compressed gradient tracking algorithm (C-GT)
that combines gradient tracking technique with communication compression. We
show that C-GT inherits the advantages of gradient tracking-based algorithms,
and in particular, achieves linear convergence rate for strongly convex and
smooth objective functions. Numerical examples further demonstrate the
efficiency and flexibility of the proposed algorithm.
