Convex Feasibility Problems

Alexander J. Zaslavski
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Alexander J. Zaslavski: The Technion - Israel Institute of Technology

Chapter Chapter 10 in Approximate Solutions of Common Fixed-Point Problems, 2016, pp 341-384 from Springer

Abstract: Abstract We use subgradient projection algorithms for solving convex feasibility problems. We show that almost all iterates, generated by a subgradient projection algorithm in a Hilbert space, are approximate solutions. Moreover, we obtain an estimate of the number of iterates which are not approximate solutions. In a finite-dimensional case, we study the behavior of the subgradient projection algorithm in the presence of computational errors. Provided computational errors are bounded, we prove that our subgradient projection algorithm generates a good approximate solution after a certain number of iterates.

Keywords: Solving Convex Feasibility Problems; Subgradient Projection Method; Computational Errors; Good Approximate Solution; Natural Numbers (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-33255-0_10

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DOI: 10.1007/978-3-319-33255-0_10

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