Publication: Automatic Differentiation and the Adjoint State Method
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Automatic Differentiation and the Adjoint State Method

- incollection -
 

Author(s)
Mark S. Gockenbach , Daniel R. Reynolds , William W. Symes

Published in
Automatic Differentiation of Algorithms: From Simulation to Optimization

Editor(s)
George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann

Year
2002

Publisher
Springer

Abstract
The C++ class fdtd uses automatic differentiation techniques to implement an abstract time stepping scheme in an object-oriented fashion, making it possible to use the resulting simulator to solve inverse or control problems. The class takes a complete specification of a single step of the scheme, and assembles from it a complete simulator, along with the linearized and adjoint simulations. The result is a (nonlinear) operator in the sense of the Hilbert Class Library, a C++ package for optimization. Performance is equivalent to that of optimized Fortran implementations.

Cross-References
Corliss2002ADo

AD Theory and Techniques
Adjoint

BibTeX
@INCOLLECTION{
         Gockenbach2002ADa,
       author = "Mark S. Gockenbach and Daniel R. Reynolds and William W. Symes",
       title = "Automatic Differentiation and the Adjoint State Method",
       pages = "161--166",
       chapter = "18",
       crossref = "Corliss2002ADo",
       booktitle = "Automatic Differentiation of Algorithms: From Simulation to Optimization",
       year = "2002",
       editor = "George Corliss and Christ{\`e}le Faure and Andreas Griewank and Laurent
         Hasco{\"e}t and Uwe Naumann",
       series = "Computer and Information Science",
       publisher = "Springer",
       address = "New York, NY",
       abstract = "The C++ class {\tt fdtd} uses automatic differentiation techniques to
         implement an abstract time stepping scheme in an object-oriented fashion, making it possible to use
         the resulting simulator to solve inverse or control problems. The class takes a complete
         specification of a {\em single step} of the scheme, and assembles from it a complete simulator,
         along with the linearized and adjoint simulations. The result is a (nonlinear) operator in the sense
         of the Hilbert Class Library, a C++ package for optimization. Performance is equivalent to that of
         optimized Fortran implementations.",
       ad_theotech = "Adjoint"
}


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