A preconditioning technique for first-order primal-dual splitting method in convex optimization

DOI10.1155/2017/3694525zbMath1426.94030OpenAlexW2567630668WikidataQ59147500 ScholiaQ59147500MaRDI QIDQ1992653

Meng Wen, Chuan-Xi Zhu, Shi-Gang Yue, Yu-Chao Tang, Ji-Gen Peng

Publication date: 5 November 2018

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/3694525

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (5)

Preconditioned three-operator splitting algorithm with applications to image restoration ⋮ Primal-dual fixed point algorithm based on adapted metric method for solving convex minimization problem with application ⋮ A fast inertial primal-dual algorithm to composite optimization models with application to image restoration problems ⋮ Efficient primal-dual fixed point algorithms with dynamic stepsize for composite convex optimization problems ⋮ Iterative methods for computing the resolvent of the sum of a maximal monotone operator and composite operator with applications

Uses Software

• UNLocBoX
• TwIST

Cites Work

• Nonlinear total variation based noise removal algorithms
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms
• Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum type monotone operators
• A first-order primal-dual algorithm for convex problems with applications to imaging
• A splitting algorithm for dual monotone inclusions involving cocoercive operators
• Fast Algorithms for Image Reconstruction with Application to Partially Parallel MR Imaging
• Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective
• Proximity algorithms for image models: denoising
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

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