3 papers
math.OC2026
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, \(μ\)-s…
math.OC2025
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 functio…
math.OC2025
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…