Decentralized ADMM for factorization-based Low-rank matrix estimation.
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- Also identified by DOI 10.1016/j.neunet.2026.109319.
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Abstract
We consider the problem of recovering a low-rank matrix in a distributed setting, based on a convex loss function and non-convex matrix factorization. We use a linearized and decentralized alternating direction method of multipliers (ADMM) algorithm to compute the consensus solution. We establish local linear convergence (up to the approximation error when the unconstrained solution is not exactly low-rank) of the method despite the optimization problem being non-convex due to the factorization. Numerical examples are presented to illustrate the performance.