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Minimization of Quasiconvex Functions

In: Convex Optimization with Computational Errors

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  • Alexander Zaslavski

    (Israel Institute of Technology)

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.

Suggested Citation

  • Alexander Zaslavski, 2020. "Minimization of Quasiconvex Functions," Springer Optimization and Its Applications, in: Convex Optimization with Computational Errors, chapter 0, pages 287-293, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-37822-6_10
    DOI: 10.1007/978-3-030-37822-6_10
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