Subgradient Projection Algorithm

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

Chapter Chapter 2 in Numerical Optimization with Computational Errors, 2016, pp 11-40 from Springer

Abstract: Abstract In this chapter we study the subgradient projection algorithm for minimization of convex and nonsmooth functions and for computing the saddle points of convex–concave functions, under the presence of computational errors. We show that our algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive 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; Convex-concave Function; Computational Errors; Small Positive Constant; Good Approximate Solution (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-30921-7_2

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DOI: 10.1007/978-3-319-30921-7_2

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