STOCHASTIC PRIMAL-DUAL HYBRID GRADIENT ALGORITHM WITH ARBITRARY SAMPLING AND IMAGING APPLICATIONS
STOCHASTIC PRIMAL-DUAL HYBRID GRADIENT ALGORITHM WITH ARBITRARY SAMPLING AND IMAGING APPLICATIONS
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DOI：
10.1137/17m1134834
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发表时间：
2018-01-01
影响因子：
3.1
通讯作者：
Schonlieb, Carola-Bibiane
中科院分区：
文献类型：
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作者：
Chambolle, Antonin;Ehrhardt, Matthias J.;Schonlieb, Carola-Bibiane
我们提出了Chambolle和Pock在2011年研究的原始-对偶混合梯度算法的随机扩展，以解决对偶变量可分离的鞍点问题。对一般的凸凹鞍点问题和部分光滑/强凸或完全光滑/强凸的问题进行了分析。我们进行分析的对偶变量的任意采样，我们得到已知的确定性结果作为一个特殊情况。我们的随机方法的几个变种显着优于各种成像任务的确定性的变体。
We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable. The analysis is carried out for general convex-concave saddle point problems and problems that are either partially smooth / strongly convex or fully smooth / strongly convex. We perform the analysis for arbitrary samplings of dual variables, and we obtain known deterministic results as a special case. Several variants of our stochastic method significantly outperform the deterministic variant on a variety of imaging tasks.