Inconsistent Convex Feasibility Problems

Alexander J. Zaslavski
Additional contact information
Alexander J. Zaslavski: Technion, Israel Institute of Technology

Chapter Chapter 7 in Solutions of Fixed Point Problems with Computational Errors, 2024, pp 337-351 from Springer

Abstract: Abstract In this chapter we study the method of cyclic projections for inconsistent convex feasibility problems in a Hilbert space under the presence of computational errors. We show that our algorithm generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Our main goal is, for a known computational error, to find out what approximate solution can be obtained and how many iterations one needs for this.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-50879-0_7

Ordering information: This item can be ordered from
http://www.springer.com/9783031508790

DOI: 10.1007/978-3-031-50879-0_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().