论文标题
随机原始二算算法的收敛特性与并行MRI的应用
Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI
论文作者
论文摘要
Chambolle等人提出了随机原始二元杂交梯度（SPDHG）。 （2018年），是一种解决一些非平滑大规模优化问题的有效算法。在本文中，我们证明了它几乎确定的凸电，但不一定是强烈的凸功能。我们还研究了其用于平行磁共振成像重建的应用，以测试SPDHG的性能。我们的数值结果表明，对于一系列设置，SPDHG的收敛速度明显快于其确定性对应物。
The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale optimization problems. In this paper we prove its almost sure convergence for convex but not necessarily strongly convex functionals. We also look into its application to parallel Magnetic Resonance Imaging reconstruction in order to test performance of SPDHG. Our numerical results show that for a range of settings SPDHG converges significantly faster than its deterministic counterpart.
