New proximal type algorithms for convex minimization and its application to image deblurring

DOI10.1007/s40314-022-02042-7OpenAlexW4298125231MaRDI QIDQ2085035

Suparat Kesornprom, Prasit Cholamjiak, Chun-Gil Park

Publication date: 14 October 2022

Published in: Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40314-022-02042-7

zbMATH Keywords

weak convergenceconvex minimization problemlinesearch ruleforward-backward methodinertial method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Nonlinear programming (90C30)

Related Items (1)

A hybrid HS-LS conjugate gradient algorithm for unconstrained optimization with applications in motion control and image recovery

Cites Work

• Unnamed Item
• Unnamed Item
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Iterative method with inertial for variational inequalities in Hilbert spaces
• An extragradient method for solving split feasibility and fixed point problems
• A multiprojection algorithm using Bregman projections in a product space
• An inertial forward-backward splitting method for solving inclusion problems in Hilbert spaces
• Convergence of a splitting inertial proximal method for monotone operators
• An accelerated viscosity forward-backward splitting algorithm with the linesearch process for convex minimization problems
• Set-valued mappings and enlargement of monotone operators.
• On the convergence of the forward–backward splitting method with linesearches
• Iterative methods for the split feasibility problem in infinite-dimensional Hilbert spaces
• The strong convergence of a KM–CQ-like algorithm for a split feasibility problem
• Solving the split feasibility problem without prior knowledge of matrix norms
• A unified treatment of some iterative algorithms in signal processing and image reconstruction
• Iterative oblique projection onto convex sets and the split feasibility problem
• Entropy-Like Proximal Methods in Convex Programming
• The relaxed CQ algorithm solving the split feasibility problem
• Improving “Fast Iterative Shrinkage-Thresholding Algorithm”: Faster, Smarter, and Greedier
• Split Feasibility and Fixed Point Problems
• Convex analysis and monotone operator theory in Hilbert spaces

This page was built for publication: New proximal type algorithms for convex minimization and its application to image deblurring