Year: 2025
Type: article
Abstract: In this work, we develop and analyze a novel Gradient Descent (GD) based solution, called Alternating GD and Minimization (AltGDmin), for efficiently solving the low rank matrix completion (LRMC) in a federated setting. Here “efficient” refers to communication-, computation- and sample- efficiency. LRMC involves recovering ann×qrank-rmatrixX* from a subset of its entries whenr≪ min(n, q). Our theoretical bounds on the sample complexity and iteration complexity of AltGDmin imply that it is the most communication-efficient solution while also been one of the most computation- and sample- efficient ones. We also extend our guarantee to the noisy LRMC setting. In addition, we show how our lemmas can be used to provide an improved sample complexity guarantee for the Alternating Minimization (AltMin) algorithm for LRMC. AltMin is one of the fastest centralized solutions for LRMC; with AltGDmin having comparable time cost even for the centralized setting.
Authors: Ahmed Ali Abbasi, Namrata Vaswani
Institution: Iowa State University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Award: G7019505065
OpenAlex ID: W4409723727