  We consider the consensual distributed optimization problem in the Riemannian
context. Specifically, the minimization of a sum of functions form is studied
where each individual function in the sum is located at the node of a network.
An algorithm, which is a direct generalization of the Euclidean case, to solve
the problem is proposed. The convergence analysis is carried out in full detail
for geodesically convex as well as non-convex functions. The algorithm is
demonstrated using some standard applications which fit the presented
framework.
