Asynchronous optimization over graphs: linear convergence under error bound conditions
We consider convex and nonconvex constrained optimization with a partially separable objective function: agents minimize the sum of local objective functions, each of which is known only by the associated agent and depends on the variables of that agent and those of a few others. This partitioned setting arises in several applications of practical interest. We propose the first distributed, asynchronous algorithm with rate guarantees for this class of problems.