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Neural Information Processing Systems 

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper analyzes ADMM with the quadratic term in the augmented Lagrangian replaced by a Bregman divergence. Authors prove convergence rate results where L2 distance (of the standard ADMM guarantee) is replaced by the corresponding Bregman divergence. Similar to mirror descent, this can yield an improvement in the dimension-dependent constants of convergence bounds. Authors then demonstrate the utility of their algorithm on synthetic instances of a specific optimization problem (mass transportation problem).