Papers › Anderson Acceleration for Nonconvex ADMM Based on Douglas-Rachford Splitting

Anderson Acceleration for Nonconvex ADMM Based on Douglas-Rachford Splitting

25 Jun 2020arXiv:2006.14539links table onlyarchive 2025-07-28

Wenqing Ouyang, Yue Peng, Yuxin Yao, Juyong Zhang, Bailin Deng

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The alternating direction multiplier method (ADMM) is widely used in computer graphics for solving optimization problems that can be nonsmooth and nonconvex. It converges quickly to an approximate solution, but can take a long time to converge to a solution of high-accuracy. Previously, Anderson acceleration has been applied to ADMM, by treating it as a fixed-point iteration for the concatenation of the dual variables and a subset of the primal variables. In this paper, we note that the equivalence between ADMM and Douglas-Rachford splitting reveals that ADMM is in fact a fixed-point iteration in a lower-dimensional space. By applying Anderson acceleration to such lower-dimensional fixed-point iteration, we obtain a more effective approach for accelerating ADMM. We analyze the convergence of the proposed acceleration method on nonconvex problems, and verify its effectiveness on a variety of computer graphics problems including geometry processing and physical simulation.

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