5 papers
Model-free LQG Control with Chance Constraints
Arunava Naha, Subhrakanti Dey
This paper studies model-free optimal control design and its convergence properties for linear time-invariant systems subject to probabilistic risk or chance constraints. In partic…
VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSI
Metehan Karatas, Subhrakanti Dey, Christian Rohner +1
Federated learning in vehicular edge networks faces major challenges in efficient resource allocation, largely due to high vehicle mobility and the presence of imperfect channel st…
On Convergence Analysis of Network-GIANT: An approximate Hessian-based fully distributed optimization algorithm
Souvik Das, Luca Schenato, Subhrakanti Dey
This paper presents a detailed convergence and performance analysis of a recently developed approximate Newton-type fully distributed optimization method for \(L\)-smooth, \(μ\)-st…
HBNET-GIANT: A communication-efficient accelerated Newton-type fully distributed optimization algorithm
Souvik Das, Luca Schenato, Subhrakanti Dey
This article presents a second-order fully distributed optimization algorithm, HBNET-GIANT, driven by heavy-ball momentum, for -smooth and -strongly convex objective function…
CoNeT-GIANT: A compressed Newton-type fully distributed optimization algorithm
Souvik Das, Subhrakanti Dey
Compression techniques are essential in distributed optimization and learning algorithms with high-dimensional model parameters, particularly in scenarios with tight communication…