In this work we propose a stochastic primal-dual preconditioned three-operator splitting algorithm 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.
翻译:在这项工作中,我们建议采用一种随机原始的、有先决条件的3个操作者分解算法,以解决一组相交的3个复合优化问题。我们提议的方案是直接的3个操作者分解SPDHG算法[Chambolle等人,2018年]。我们提供理论趋同分析,显示ERGodic O(1/K)的趋同率,并展示我们在成像反问题方面的做法的有效性。