Nondifferentiable optimization via smooth approximation: General analytical approach

Joseph Kreimer, Reuven Y. Rubinstein

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

In this paper we present a method for nondifferentiable optimization, based on smoothed functionals which preserve such useful properties of the original function as convexity and continuous differentiability. We show that smoothed functionals are convenient for implementation on computers. We also show how some earlier results in nondifferentiable optimization based on smoothing-out of kink points can be fitted into the framework of smoothed functionals. We obtain polynomial approximations of any order from smoothed functionals with kernels given by Beta distributions. Applications of smoothed functionals to optimization of min-max and other problems are also discussed.

Original languageEnglish
Pages (from-to)97-119
Number of pages23
JournalAnnals of Operations Research
Volume39
Issue number1
DOIs
StatePublished - 1 Dec 1992

Keywords

  • Min-max problems
  • digital signal processing
  • nondifferentiable optimization
  • polynomial approximations
  • smoothed functionals

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research

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