Considerable research efforts have been directed at implementing the Faddeeva function w(z) and its derivatives with respect to z, but these did not consider the key computing issue of a possible dependence of z on some variable t. The general case is to differentiate the compound function w(z(t))=w∘z(t) with respect to t by applying the chain rule for a first order derivative, or Faà di Bruno’s formula for higher-order ones. Higher-order automatic differentiation (HOAD) is an efficient and accurate technique for derivative calculation along scientific computing codes. Although codes are available for w(z), a special symbolic HOAD is required to compute accurate higher-order derivatives for w∘z(t) in an efficient manner. A thorough evaluation is carried out considering a nontrivial case study in optics to support this assertion. Program summaryProgram title: HOAD_MathFunCatalogue identifier: AFAG_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AFAG_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: yesNo. of lines in distributed program, including test data, etc.: 4737No. of bytes in distributed program, including test data, etc.: 103450Distribution format: tar.gzProgramming language: Fortran 90.Computer: Non-specific.Operating system: Non-specific.RAM: 1 megabyteClassification: 4.12.External routines: Algorithm 680 [8], Mathematical functions (included)Nature of problem:General optimized higher-order automatic differentiation of mathematical functions. Complex refractive index as a case study.Solution method:Higher-order differentiation for the general second order ordinary equation defining the mathematical functions. Optimized operator overloading recurrence formula.Unusual features:Automatic differentiation, Quadratic complexity.Running time:1 second.