The rate of convergence for the cyclic projections algorithm. II: Norms of nonlinear operators

DOI10.1016/j.jat.2006.02.006zbMath1109.41017OpenAlexW1975567493MaRDI QIDQ855484

Frank Deutsch, Hein S. Hundal

Publication date: 7 December 2006

Published in: Journal of Approximation Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jat.2006.02.006

zbMATH Keywords

algorithmsconvex sets

Mathematics Subject Classification ID

Asymptotic approximations, asymptotic expansions (steepest descent, etc.) (41A60) Best approximation, Chebyshev systems (41A50)

Related Items (13)

Local linear convergence for alternating and averaged nonconvex projections ⋮ The rate of convergence for the cyclic projections algorithm. I: Angles between convex sets ⋮ Restricted normal cones and the method of alternating projections: applications ⋮ Random algorithms for convex minimization problems ⋮ Unnamed Item ⋮ The method of alternating relaxed projections for two nonconvex sets ⋮ Convergence properties of dynamic string-averaging projection methods in the presence of perturbations ⋮ Characterizing arbitrarily slow convergence in the method of alternating projections ⋮ Unnamed Item ⋮ Stochastic First-Order Methods with Random Constraint Projection ⋮ On angles between convex sets in Hilbert spaces ⋮ The rate of convergence for the cyclic projections algorithm. III: Regularity of convex sets ⋮ Weak, Strong, and Linear Convergence of a Double-Layer Fixed Point Algorithm

Cites Work

• The rate of convergence for the cyclic projections algorithm. I: Angles between convex sets
• The rate of convergence for the cyclic projections algorithm. III: Regularity of convex sets
• Error bounds for the method of alternating projections
• On the convergence of von Neumann's alternating projection algorithm for two sets
• An alternating projection that does not converge in norm
• On Projection Algorithms for Solving Convex Feasibility Problems
• Theory of Reproducing Kernels
• Étude sur les variétés et les opérateurs de Julia, avec quelques applications
• Best approximation in inner product spaces
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