  The paper proves convergence to global optima for a class of distributed
algorithms for nonconvex optimization in network-based multi-agent settings.
Agents are permitted to communicate over a time-varying undirected graph. Each
agent is assumed to possess a local objective function (assumed to be smooth,
but possibly nonconvex). The paper considers algorithms for optimizing the sum
function. A distributed algorithm of the consensus+innovations type is proposed
which relies on first-order information at the agent level. Under appropriate
conditions on network connectivity and the cost objective, convergence to the
set of global optima is achieved by an annealing-type approach, with decaying
Gaussian noise independently added into each agent's update step. It is shown
that the proposed algorithm converges in probability to the set of global
minima of the sum function.
