BibTeX
@ARTICLE{
         Bischof2003ADf,
       title = "Automatic Differentiation for {MATLAB} Programs",
       journal = "Proceedings in Applied Mathematics and Mechanics",
       year = "2003",
       pages = "50--53",
       abstract = "Derivative information is required in numerous applications, including sensitivity
         analysis and numerical optimization. For simple functions, symbolic differentiation---done either
         manually or with a computer algebra system---can provide the derivatives, whereas divided
         differences (DD) have been used traditionally for functions defined by (potentially very complex)
         computer programs, even if only approximate values can be obtained this way. An alternative approach
         for such functions is automatic differentiation (AD), yielding exact derivatives at often lower cost
         than DD, and without restrictions on the program complexity. In this paper we compare the
         functionality and describe the use of ADMIT/ADMAT and ADiMat. These two AD tools provide derivatives
         for programs written in the MATLAB language, which is widely used for prototype and production
         software in scientific and engineering applications. While ADMIT/ADMAT implements a pure operator
         overloading approach of AD, ADiMat also employes source transformation techniques.",
       ad_tools = "ADiMat",
       author = "Christian H.~Bischof and Bruno Lang and Andre Vehreschild",
       volume = "2",
       number = "1",
       doi = "10.1002/pamm.200310013"
}