A Riemannian gossip approach to decentralized matrix completion

2016/05/23 by Bamdev Mishra, Mishra, Bamdev, Hiroyuki Kasai +3 · 1 citation
Computer Science · Engineering · #Cellular Automata and Applications #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1605.06968

openalex publication_date 2016/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem. The proposed approach is on the Riemannian Grassmann manifold that allows local matrix completion by different agents while achieving asymptotic consensus on the global low-rank factors. The resulting approach is scalable and parallelizable. Our numerical experiments show the good performance of the proposed algorithms on various benchmarks.

Citations

• Low-rank matrix completion via preconditioned optimization on the Grassmann manifold

Cited by

• DeMuon: A Decentralized Muon for Matrix Optimization over Graphs

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