A Stochastic Variance Reduced Primal Dual Fixed Point Method for Linearly Constrained Separable Optimization

DOI10.1137/20M1354398zbMath1474.65042arXiv2007.11783OpenAlexW3200236211MaRDI QIDQ5860367

Ya-Nan Zhu, Xiaoqun Zhang

Publication date: 19 November 2021

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.11783

zbMATH Keywords

image restorationstochastic variance reduced gradientprimal dual fixed point method

Mathematics Subject Classification ID

Convex programming (90C25) Numerical optimization and variational techniques (65K10) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• LIBSVM
• ElemStatLearn
• Saga

Cites Work

• Unnamed Item
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• On the ergodic convergence rates of a first-order primal-dual algorithm
• Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum type monotone operators
• Stochastic primal dual fixed point method for composite optimization
• On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Convergence of stochastic proximal gradient algorithm
• On the global and linear convergence of the generalized alternating direction method of multipliers
• A Generalized Forward-Backward Splitting
• On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
• Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
• Proximity algorithms for image models: denoising
• A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science
• New Proximal Point Algorithms for Convex Minimization
• Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
• A primal–dual fixed point algorithm for convex separable minimization with applications to image restoration
• Optimal Primal-Dual Methods for a Class of Saddle Point Problems
• A Proximal Stochastic Gradient Method with Progressive Variance Reduction
• Signal Recovery by Proximal Forward-Backward Splitting
• Convex analysis and monotone operator theory in Hilbert spaces