Convergence Analysis of the Relaxed Douglas--Rachford Algorithm
DOI10.1137/18M1229638MaRDI QIDQ5217595
D. Russell Luke, Anna-Lena Martins
Publication date: 25 February 2020
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1811.11590
fixed pointprojectionlinear convergencenonconvexmetric subregularitysubtransversalityinconsistent feasibility problemrelaxed averaged alternating reflectionssuperregular
Numerical mathematical programming methods (65K05) Sensitivity, stability, well-posedness (49K40) Nonconvex programming, global optimization (90C26) Numerical optimization and variational techniques (65K10) Numerical methods based on necessary conditions (49M05) Set-valued and variational analysis (49J53) Numerical methods of relaxation type (49M20)
Related Items (5)
Cites Work
- Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems
- Finding best approximation pairs relative to two closed convex sets in Hilbert spaces
- There is no variational characterization of the cycles in the method of periodic projections
- Local linear convergence for alternating and averaged nonconvex projections
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- Linear convergence of the generalized Douglas-Rachford algorithm for feasibility problems
- Necessary conditions for linear convergence of iterated expansive, set-valued mappings
- Set regularities and feasibility problems
- On the local convergence of the Douglas-Rachford algorithm
- Linear convergence of the Douglas–Rachford method for two closed sets
- The Douglas--Rachford Algorithm for Two (Not Necessarily Intersecting) Affine Subspaces
- On the Numerical Solution of Heat Conduction Problems in Two and Three Space Variables
- Finding Best Approximation Pairs Relative to a Convex and Prox-Regular Set in a Hilbert Space
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Relaxed averaged alternating reflections for diffraction imaging
- Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisons
- Quantitative Convergence Analysis of Iterated Expansive, Set-Valued Mappings
- Nonconvex Notions of Regularity and Convergence of Fundamental Algorithms for Feasibility Problems
- Implicit Functions and Solution Mappings
- Strong CHIP, normality, and linear regularity of convex sets
- EXISTENCE AND APPROXIMATION OF SOLUTIONS OF NONLINEAR VARIATIONAL INEQUALITIES
- Weak convergence of the sequence of successive approximations for nonexpansive mappings
- Functional Operators (AM-22), Volume 2
- Convex analysis and monotone operator theory in Hilbert spaces
- Best approximation in inner product spaces
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