Hybrid Moreau’s Proximal Algorithms and Convergence Theorems for Minimization Problems in Hilbert Spaces with Applications

DOI10.1080/01630563.2018.1501382zbMath1418.47006OpenAlexW2895867765WikidataQ129122629 ScholiaQ129122629MaRDI QIDQ4622805

Chih-Sheng Chuang

Publication date: 18 February 2019

Published in: Numerical Functional Analysis and Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01630563.2018.1501382

zbMATH Keywords

strong convergenceweak convergenceprojectionsplit feasibility problemproximal gradient algorithm

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Variational inequalities (49J40) Iterative procedures involving nonlinear operators (47J25) Contraction-type mappings, nonexpansive mappings, (A)-proper mappings, etc. (47H09)

Cites Work

• Strong convergence of projected subgradient methods for nonsmooth and nonstrictly convex minimization
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• Fixed point optimization algorithm and its application to network bandwidth allocation
• Convexly constrained linear inverse problems: iterative least-squares and regularization
• Iteration methods for convexly constrained ill-posed problems in hilbert space
• Iterative oblique projection onto convex sets and the split feasibility problem
• Solving monotone inclusions via compositions of nonexpansive averaged operators
• A fast quadratic programming algorithm for positive signal restoration
• FIXED-POINT THEOREMS FOR NONCOMPACT MAPPINGS IN HILBERT SPACE
• Signal Recovery by Proximal Forward-Backward Splitting
• Proximité et dualité dans un espace hilbertien
• Weak and strong convergence theorems for fixed points of asymptotically nonexpansive mappings
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