报告人：练恒 教授 (香港城市大学)

时间：2025年04月25日 10:30-

地点：数统公司LD402

摘要：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 unstrained solution is not exactly low-rank) of the method despite the optimization problem is non-convex due to the factorization. Numerical examples are presented to illustrate the performance.

邀请人: 夏小超

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