Abstract
In this chapter we study minimization of a quasiconvex function. Our algorithm has two steps. In each of these two steps there is a computational error. In general, these two computational errors are different. We show that our algorithm generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if we know the computational errors for the two steps of our algorithm, we find out what approximate solution can be obtained and how many iterates one needs for this.
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References
Konnov IV (2003) On convergence properties of a subgradient method. Optim Methods Softw 18:53–62
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J. Zaslavski, A. (2020). Minimization of Quasiconvex Functions. In: Convex Optimization with Computational Errors. Springer Optimization and Its Applications, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-37822-6_10
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DOI: https://doi.org/10.1007/978-3-030-37822-6_10
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37821-9
Online ISBN: 978-3-030-37822-6
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