A relaxed parameter condition for the primal-dual hybrid gradient method for saddle-point problem
From MaRDI portal
Publication:2097452
DOI10.3934/jimo.2022008OpenAlexW4210317844MaRDI QIDQ2097452
Yuqian Kong, Xiayang Zhang, Shanshan Liu, Yuan Shen
Publication date: 14 November 2022
Published in: Journal of Industrial and Management Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3934/jimo.2022008
saddle-point problemprimal-dual hybrid gradient methodparameter conditionChambolle-Pock first-order method
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods for variational inequalities and related problems (65K15)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Computing the nearest correlation matrix--a problem from finance
- Nonlinear total variation based noise removal algorithms
- On the ergodic convergence rates of a first-order primal-dual algorithm
- Gradient methods for minimizing composite functions
- An improved first-order primal-dual algorithm with a new correction step
- Strongly convex programming for exact matrix completion and robust principal component analysis
- A unified primal-dual algorithm framework based on Bregman iteration
- A primal-dual prediction-correction algorithm for saddle point optimization
- An algorithm for total variation minimization and applications
- 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
- Multiplier and gradient methods
- 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
- Linearized Bregman iterations for compressed sensing
- A Singular Value Thresholding Algorithm for Matrix Completion
- A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science
- A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
- Linearized Bregman Iterations for Frame-Based Image Deblurring
- The Split Bregman Method for L1-Regularized Problems
- Inertial Proximal ADMM for Linearly Constrained Separable Convex Optimization
- Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
- A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- Inertial, Corrected, Primal-Dual Proximal Splitting
- On the Convergence of Primal-Dual Hybrid Gradient Algorithm
- Optimal Primal-Dual Methods for a Class of Saddle Point Problems
