A primal-dual fixed point algorithm for multi-block convex minimization

A primal-dual fixed point algorithm for multi-block convex minimization
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多块凸最小化的原对偶不动点算法

DOI:
10.4208/jcm.1612-m2016-0536
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发表时间:
2016-02
影响因子:
0.9
通讯作者:
Zhang Xiaoqun
Zhang Xiaoqun
中科院分区:
数学4区
文献类型:
--
作者:
Chen Peijun;Huang Jianguo;Zhang Xiaoqun

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本文推广了文献[5]中提出的一种原始-对偶不动点算法(PDFP)来求解信号处理和成像科学中出现的两类可分离多块最小化问题。这项工作显示了应用PDFP算法的多块问题的灵活性,并说明如何实际和完全解耦的计划,可以得出,特别是对于大规模问题的并行实现。与交替方向乘法器(ADMM)的连接和比较。通过带约束的稀疏正则化最小二乘模型的经典算例,我们展示了如何通过不同的方法将问题分解得到不同的算法。特别是,对于一类线性约束问题,这是非常感兴趣的多块ADMM的上下文中,可以解决PDFP的收敛性保证。最后,通过实验验证了PDFP算法所得到的几种方案的性能。
We extend a primal-dual fixed point algorithm (PDFP) proposed in [5] to solve two kinds of separable multi-block minimization problems, arising in signal processing and imaging science. This work shows the flexibility of applying PDFP algorithm to multi-block problems and illustrate how practical and fully decoupled schemes can be derived, especially for parallel implementation of large scale problems. The connections and comparisons to the alternating direction method of multiplier (ADMM) are also present. We demonstrate how different algorithms can be obtained by splitting the problems in different ways through the classic example of sparsity regularized least square model with constraint. In particular, for a class of linearly constrained problems, which are of great interest in the context of multi-block ADMM, can be solved by PDFP with a guarantee of convergence. Finally, some experiments are provided to illustrate the performance of several schemes derived by the PDFP algorithm.
DOI: 10.1186/s13663-016-0543-2
发表时间: 2015-12
影响因子: --
作者:
Chen Peijun;Huang Jianguo;Zhang Xiaoqun
通讯作者: Zhang Xiaoqun
DOI: --
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