Continuous Subgradient Method

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

Chapter Chapter 14 in Numerical Optimization with Computational Errors, 2016, pp 225-238 from Springer

Abstract: Abstract In this chapter we study the continuous subgradient algorithm for minimization of convex 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 much time one needs for this.

Keywords: Hilbert Space; Banach Space; Approximate Solution; Convex Function; Convex Hull (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_14

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DOI: 10.1007/978-3-319-30921-7_14

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