Motivated by large-scale optimization problems arising in the context of machine learning,
there have been several advances in the study of asynchronous parallel and distributed …

In this letter, we study distributed optimization, where a network of agents, abstracted as a
directed graph, collaborates to minimize the average of locally known convex functions …

Decentralized optimization to minimize a finite sum of functions, distributed over a network of
nodes, has been a significant area within control and signal-processing research due to its …

The detection of gravitational waves with ground-based laser-interferometric detectors
requires sensitivity to changes in distance much smaller than the diameter of atomic nuclei …

Methods for distributed optimization have received significant attention in recent years owing
to their wide applicability in various domains including machine learning, robotics, and …

We consider a multiagent framework for distributed optimization where each agent has
access to a local smooth strongly convex function, and the collective goal is to achieve …

Carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because
of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the …

One of the most important problems in the field of distributed optimization is the problem of
minimizing a sum of local convex objective functions over a networked system. Most of the …

We consider the standard model of distributed optimization of a sum of functions F (z)= Σ ni=
1 fi (z), where node i in a network holds the function fi (z). We allow for a harsh network …

We introduce the primal-dual quasi-Newton (PD-QN) method as an approximated second
order method for solving decentralized optimization problems. The PD-QN method performs …