BibTeX
@INCOLLECTION{
         Hascoet2005TAD,
       title = "The Adjoint Data-Flow Analyses: {F}ormalization, Properties, and Applications",
       editor = "H. M. B{\"u}cker and G. Corliss and P. Hovland and U. Naumann and B.
         Norris",
       booktitle = "Automatic Differentiation: {A}pplications, Theory, and Implementations",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       ad_area = "General",
       ad_tools = "TAPENADE",
       ad_theotech = "Data Flow Analysis, Adjoint",
       year = "2005",
       author = "Laurent Hasco{\"e}t and Mauricio Araya-Polo",
       abstract = "Automatic Differentiation (AD) is a program transformation that yields derivatives.
         Building efficient derivative programs requires complex and specific static analysis algorithms to
         reduce run time and memory usage. Focusing on the reverse mode of AD, which computes adjoint
         programs, we specify jointly the central static analyses that are required to generate an efficient
         adjoint code. We use a set-based formalization from classical data-flow analysis to specify Adjoint
         Liveness, Adjoint Write, and To Be Recorded analyses, and their mutual influences, taking into
         account the specific structure of adjoint programs. We give illustrations on examples taken from
         real numerical programs, that we differentiate with our AD tool TAPENADE, which implements these
         analyses.",
       crossref = "Bucker2005ADA",
       pages = "135--146",
       doi = "10.1007/3-540-28438-9_12"
}