【摘 要】
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The alternating direction method of multipliers(ADMM)is being widely used for various convex programming models with separable structures arising in specifically many scientific computing areas.In thi
【机 构】
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Tianjin University
【出 处】
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2016信号处理、优化与控制国际研讨会(2016 International Workshop on Signal Pr
论文部分内容阅读
The alternating direction method of multipliers(ADMM)is being widely used for various convex programming models with separable structures arising in specifically many scientific computing areas.In this paper,we suggest applying a rule proposed recently by Chambolle and Pock to iteratively update the penalty parameter and show that ADMM with this adaptive penalty parameter has a worst-case O(1/n^2)convergence rate.Without strong convexity requirement on the objective function,our assumptions on the model are mild and can be satisfied by some representative applications.
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