172 citations · 516 across the 28 of their papers we have counts for
6 papers · 1 filter
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
Brian Swenson, Soummya Kar, H. Vincent Poor +2
The article discusses distributed gradient-descent algorithms for computing local and global minima in nonconvex optimization. For local optimization, we focus on distributed stoch…
Primal-dual methods for large-scale and distributed convex optimization and data analytics
Dusan Jakovetic, Dragana Bajovic, Joao Xavier +1
The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given "difficult" (constrained) problem via finding solutions of a sequence of "easier"(often u…
Resilient Distributed Recovery of Large Fields
Yuan Chen, Soummya Kar, José M. F. Moura
This paper studies the resilient distributed recovery of large fields under measurement attacks, by a team of agents, where each measures a small subset of the components of a larg…
Resilient Distributed Field Estimation
Yuan Chen, Soummya Kar, José M. F. Moura
We study resilient distributed field estimation under measurement attacks. A network of agents or devices measures a large, spatially distributed physical field parameter. An adver…
Resilient Distributed Parameter Estimation with Heterogeneous Data
Yuan Chen, Soummya Kar, José M. F. Moura
This paper studies resilient distributed estimation under measurement attacks. A set of agents each makes successive local, linear, noisy measurements of an unknown vector field co…
Resilient Distributed Estimation: Sensor Attacks
Yuan Chen, Soummya Kar, José M. F. Moura
This paper studies multi-agent distributed estimation under sensor attacks. Individual agents make sensor measurements of an unknown parameter belonging to a compact set, and, at e…