On Distributed Stochastic Gradient Algorithms for Global Optimization

Understand

The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems.

• It is known that distributed gradient-descent-type algorithms can achieve convergence to the set of global minima by adding slowly decaying Gaussian noise in order escape local minima.
• However, the technical assumptions under which convergence is known to occur can be restrictive in practice.
• In particular, in known convergence results, the local objective functions possessed by agents are required to satisfy a highly restrictive bounded-gradient-dissimilarity condition.