Dynamic String-Averaging Subgradient Projection Algorithm

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

Chapter Chapter 12 in Approximate Solutions of Common Fixed-Point Problems, 2016, pp 411-446 from Springer

Abstract: Abstract In this chapter we study convergence of dynamic string-averaging subgradient projection algorithms for solving convex feasibility problems in a general Hilbert space. Our goal is to obtain an approximate solution of the problem in the presence of computational errors. We show that our subgradient projection algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a constant. Moreover, for a known computational error, we find out what an approximate solution can be obtained and how many iterates one needs for this.

Keywords: Subgradient Projection Algorithm; Solving Convex Feasibility Problems; General Hilbert Space; Computational Errors; Good Approximate Solution (search for similar items in EconPapers)
Date: 2016
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:978-3-319-33255-0_12

Ordering information: This item can be ordered from
http://www.springer.com/9783319332550

DOI: 10.1007/978-3-319-33255-0_12

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 ().