论文标题
随机原始偶二三操作员分裂算法，扩展到逐个定期的正规化算法
Stochastic Primal-Dual Three Operator Splitting Algorithm with Extension to Equivariant Regularization-by-Denoising
论文作者
论文摘要
在这项工作中，我们提出了一种随机原始双偶三重操作员分裂算法（TOS-SPDHG），用于解决一类凸的三复合优化问题。我们提出的方案是SPDHG算法的直接三操作员分裂扩展[Chambolle等。 2018]。我们提供了理论上的收敛分析，该分析显示了Ergodic $ O（1/K）$收敛率，并证明了我们方法在成像反问题中的有效性。此外，我们进一步提出了TOS-SPDHG-RED和TOS-SPDHG-ERER，这些tos-spdhg-ered利用逐个定期化（红色）框架来利用预处理的深层denoinging网络作为先验。
In this work we propose a stochastic primal-dual three-operator splitting algorithm (TOS-SPDHG) for solving a class of convex three-composite optimization problems. Our proposed scheme is a direct three-operator splitting extension of the SPDHG algorithm [Chambolle et al. 2018]. We provide theoretical convergence analysis showing ergodic $O(1/K)$ convergence rate, and demonstrate the effectiveness of our approach in imaging inverse problems. Moreover, we further propose TOS-SPDHG-RED and TOS-SPDHG-eRED which utilizes the regularization-by-denoising (RED) framework to leverage pretrained deep denoising networks as priors.
