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Iterative Subgradient Projection Algorithm

In: Approximate Solutions of Common Fixed-Point Problems

Author

Listed:
  • Alexander J. Zaslavski

    (The Technion - Israel Institute of Technology)

Abstract

In this chapter we study convergence of iterative 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.

Suggested Citation

  • Alexander J. Zaslavski, 2016. "Iterative Subgradient Projection Algorithm," Springer Optimization and Its Applications, in: Approximate Solutions of Common Fixed-Point Problems, chapter 0, pages 385-409, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-33255-0_11
    DOI: 10.1007/978-3-319-33255-0_11
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