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Automatic Differentiation Tools in Optimization Software

- incollection -
 

Author(s)
Jorge J. Moré

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
We discuss the role of automatic differentiation tools in optimization software. We emphasize issues that are important to large-scale optimization and that have proved useful in the installation of nonlinear solvers in the NEOS Server. Our discussion centers on the computation of the gradient and Hessian matrix for partially separable functions and shows that the gradient and Hessian matrix can be computed with guaranteed bounds in time and memory requirements.

Cross-References
Corliss2002ADo

BibTeX
@INCOLLECTION{
         More2002ADT,
       author = "Jorge J. Mor{\'e}",
       title = "Automatic Differentiation Tools in Optimization Software",
       pages = "25--34",
       chapter = "2",
       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 = "We discuss the role of automatic differentiation tools in optimization software. We
         emphasize issues that are important to large-scale optimization and that have proved useful in the
         installation of nonlinear solvers in the NEOS Server. Our discussion centers on the computation of
         the gradient and Hessian matrix for partially separable functions and shows that the gradient and
         Hessian matrix can be computed with guaranteed bounds in time and memory requirements."
}


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