Accelerating ADMM for Efficient Simulation and Optimization

Zhang, Jie; Peng, Yue; Ouyang, Wenqing; Deng, Bailin

doi:10.48550/arxiv.1909.00470

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2019

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“…AA has also seen success in nonconvex settings. Notably, [ZPOD19] applies AA to ADMM and establishes convergence of the accelerated algorithm to a stationary point under certain smoothness conditions.…”

Section: Related Workmentioning

confidence: 99%

Anderson Accelerated Douglas-Rachford Splitting

Fu,

Zhang,

Boyd

2019

Preprint

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We consider the problem of non-smooth convex optimization with general linear constraints, where the objective function is only accessible through its proximal operator. This problem arises in many different fields such as statistical learning, computational imaging, telecommunications, and optimal control. To solve it, we propose an Anderson accelerated Douglas-Rachford splitting (A2DR) algorithm, which we show either globally converges or provides a certificate of infeasibility/unboundedness under very mild conditions. Applied to a block separable objective, A2DR partially decouples so that its steps may be carried out in parallel, yielding an algorithm that is fast and scalable to multiple processors. We describe an open-source implementation and demonstrate its performance on a wide range of examples.

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Section: Related Workmentioning

confidence: 99%