  This work develops a provably accurate fully-decentralized alternating
projected gradient descent (GD) algorithm for recovering a low rank (LR) matrix
from mutually independent projections of each of its columns, in a fast and
communication-efficient fashion. To our best knowledge, this work is the first
attempt to develop a provably correct decentralized algorithm (i) for any
problem involving the use of an alternating projected GD algorithm; (ii) and
for any problem in which the constraint set to be projected to is a non-convex
set.
