  This paper considers a distributed optimization problem over a multi-agent
network, in which the objective function is a sum of individual cost functions
at the agents. We focus on the case when communication between the agents is
described by a \emph{directed} graph. Existing distributed optimization
algorithms for directed graphs require at least the knowledge of the neighbors'
out-degree at each agent (due to the requirement of column-stochastic
matrices). In contrast, our algorithm requires no such knowledge. Moreover, the
proposed algorithm achieves the best known rate of convergence for this class
of problems, $O(\mu^k)$ for $0<\mu<1$, where $k$ is the number of iterations,
given that the objective functions are strongly-convex and have
Lipschitz-continuous gradients. Numerical experiments are also provided to
illustrate the theoretical findings.
