This paper considers nonconvex distributed constrained optimization over networks,
modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic …

Recently there has been a significant amount of research on developing consensus based
algorithms for distributed optimization motivated by applications that vary from large scale …

This paper discusses practical consensus-based distributed optimization algorithms. In
consensus-based optimization algorithms, nodes interleave local gradient descent steps …

This paper presents a general class of gossip-based averaging algorithms, which are
inspired from Uniform Gossip. While Uniform Gossip works synchronously on complete …

Federated learning has become increasingly important for modern machine learning,
especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the …

This paper deals with the problem of designing a distributed fault detection and isolation
methodology for nonlinear uncertain large-scale discrete-time dynamical systems. As a …

This paper proposes a distributed dual gradient tracking algorithm (DDGT) to solve resource
allocation problems over an unbalanced network, where each node in the network holds a …

This paper deals with the design and analysis of dynamical systems on directed graphs
(digraphs) that achieve weight-balanced and doubly stochastic assignments. Weight …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …

We study nonconvex distributed optimization in multiagent networks where the
communications between nodes is modeled as a time-varying sequence of arbitrary …