A Generalized Primal-Dual Algorithm with Improved Convergence Condition for Saddle Point Problems
DOI10.1137/21M1453463zbMath1494.94009arXiv2112.00254OpenAlexW4226369898MaRDI QIDQ5094636
Feng Ma, Shengjie Xu, Bing-sheng He, Xiao-Ming Yuan
Publication date: 4 August 2022
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2112.00254
image processingconvex programmingassignment problemsaddle point problemprimal-dual algorithmconvergence condition
Convex programming (90C25) Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items (2)
Uses Software
Cites Work
- Nonlinear total variation based noise removal algorithms
- On the ergodic convergence rates of a first-order primal-dual algorithm
- A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms
- An algorithmic framework of generalized primal-dual hybrid gradient methods for saddle point problems
- A first-order primal-dual algorithm for convex problems with applications to imaging
- On the convergence of primal-dual hybrid gradient algorithms for total variation image restoration
- A splitting algorithm for dual monotone inclusions involving cocoercive operators
- On convergence of the Arrow-Hurwicz method for saddle point problems
- On the equivalence of the primal-dual hybrid gradient method and Douglas-Rachford splitting
- On non-ergodic convergence rate of Douglas-Rachford alternating direction method of multipliers
- Linear and nonlinear programming
- Atomic Decomposition by Basis Pursuit
- On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
- Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective
- A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Monotone Operators and the Proximal Point Algorithm
- First-Order Methods in Optimization
- A First-Order Primal-Dual Algorithm with Linesearch
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- Efficient and Convergent Preconditioned ADMM for the Potts Models
- Optimal proximal augmented Lagrangian method and its application to full Jacobian splitting for multi-block separable convex minimization problems
- On the Convergence of Primal-Dual Hybrid Gradient Algorithm
- An introduction to continuous optimization for imaging
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: A Generalized Primal-Dual Algorithm with Improved Convergence Condition for Saddle Point Problems
