Relax, and Accelerate: A Continuous Perspective on ADMM
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The acceleration technique first introduced by Nesterov for gradient descent is widely used in many machine learning applications, however it is not yet well-understood. Recently, significant progress has been made to close this understanding gap by using a continuous-time dynamical system perspective associated with gradient-based methods. In this paper, we extend this perspective by considering the continuous limit of the family of relaxed Alternating Direction Method of Multipliers (ADMM). We also introduce two new families of relaxed and accelerated ADMM algorithms, one follows Nesterov's acceleration approach and the other is inspired by Polyak's Heavy Ball method, and derive the continuous limit of these families of relaxed and accelerated algorithms as differential equations. Then, using a Lyapunov stability analysis of the dynamical systems, we obtain rate-of-convergence results for convex and strongly convex objective functions.
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