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An augmented-Lagrangian-based parallel splitting method for linearly constrained separate convex programming with applications to image processing

机译:线性约束分离凸规划的基于增强拉格朗日并行分割方法及其在图像处理中的应用

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摘要

This paper considers the linearly constrained convex programming model with the separate structure that its objective function is in the form of the sum of the sum of finitely many individual functions without crossed variables. Obviously, the classical augmented Lagrangian method (ALM) is directly applicable, without any adaption in accordance with the particular structure of the model under consideration.
机译:本文考虑具有独立结构的线性约束凸规划模型,其目标函数为有限多个无交叉变量的单个函数之和的形式。显然,经典的增强拉格朗日方法(ALM)可以直接应用,而无需根据所考虑模型的特定结构进行任何调整。

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