  Bregman parallel direction method of multipliers (BPDMM) efficiently solves
distributed optimization over a network, which arises in a wide spectrum of
collaborative multi-agent learning applications. In this paper, we generalize
BPDMM to stochastic BPDMM, where each iteration only solves local optimization
on a randomly selected subset of nodes rather than all the nodes in the
network. Such generalization reduce the need for computational resources and
allows applications to larger scale networks. We establish both the global
convergence and the \(O(1/T)\) iteration complexity of stochastic BPDMM. We
demonstrate our results via numerical examples.
