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Generalizations of the Alternating Direction Method of Multipliers for Large-Scale and Distributed Optimization
The alternating direction method of multipliers (ADMM) has been revived in recent years due to its effectiveness at solving many large-scale and distributed optimization problems, particularly arising from the areas of ...
On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers
The formulation min f(x)+g(y) subject to Ax+By=b arises in many application areas such as signal processing, imaging and image processing, statistics, and machine learning either naturally or after variable splitting. In ...
Group Sparse Optimization by Alternating Direction Method
This paper proposes efficient algorithms for group sparse optimization with mixed L21-regularization, which arises from the reconstruction of group sparse signals in compressive sensing, and the group Lasso problem in ...