Abstract

The alternating direction method of multipliers (ADMM), also well known as a special split Bregman algorithm in imaging, is being popularly used in many areas including the image processing field. One useful modification is the symmetric version of the original ADMM, which updates the Lagrange multiplier twice at each iteration and thus the variables are treated in a symmetric manner. The symmetric version of ADMM, however, is not necessarily convergent. It was recently found that the convergence of symmetric ADMM can be sufficiently ensured if both the step sizes for updating the Lagrange multiplier are shrunk conservatively. Despite the theoretical significance in ensuring convergence, however, smaller step sizes should be strongly avoided in practice. On the contrary, we actually have the desire of seeking larger step sizes whenever possible in order to accelerate the numerical performance. Another technique leading to numerical acceleration of ADMM is enlarging its step size by a constant originally p...

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