Differentiation via Logarithmic Expansions

Michael C. Fu, Bernd Heidergott*, Haralambie Leahu, Felisa J. Vázquez-Abad

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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In this note, we introduce a new finite difference approximation called the Black-Box Logarithmic Expansion Numerical Derivative (BLEND) algorithm, which is based on a formal logarithmic expansion of the differentiation operator. BLEND capitalizes on parallelization and provides derivative approximations of arbitrary precision, i.e., our analysis can be used to determine the number of terms in the series expansion to guarantee a specified number of decimal places of accuracy. Furthermore, in the vector setting, the complexity of the resulting directional derivative is independent of the dimension of the parameter.

Original languageEnglish
Article number1950034
Pages (from-to)1-5
Number of pages5
JournalAsia-Pacific Journal of Operational Research
Issue number1
Publication statusPublished - 1 Feb 2020


  • directional derivative
  • Finite difference algorithm
  • numerical differentiation
  • sensitivity analysis
  • Taylor series expansions


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