Cimmino Gradient Projection Algorithm

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

Chapter Chapter 8 in Optimization on Solution Sets of Common Fixed Point Problems, 2021, pp 289-310 from Springer

Abstract: Abstract In this chapter we consider a minimization of a convex smooth function on a solution set of a convex feasibility problem in a general Hilbert space using the Cimmino gradient projection algorithm. Our goal is to obtain a good approximate solution of the problem in the presence of computational errors. We show that an algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a small constant. Moreover, if we known computational errors for our algorithm, we find out what an approximate solution can be obtained and how many iterates one needs for this.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-78849-0_8

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DOI: 10.1007/978-3-030-78849-0_8

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