  In this work, we study the classical distributed optimization problem over
digraphs, where the objective function is a sum of smooth local functions.
Inspired by the implicit tracking mechanism proposed in our earlier work, we
develop a unified algorithmic framework from a pure primal perspective, i.e.,
UGT, which is essentially a generalized gradient tracking method and can unify
most existing distributed optimization algorithms with constant step-sizes. It
is proved that two variants of UGT can both achieve linear convergence if the
global objective function is strongly convex. Finally, the performance of UGT
is evaluated by numerical experiments.
