Publication: Application of Targeted Automatic Differentiation to Large-Scale Dynamic Optimization
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Application of Targeted Automatic Differentiation to Large-Scale Dynamic Optimization

- incollection -
 

Area
Differential-Algebraic Equation, Dynamic Optimization

Author(s)
Derya B. Özyurt , Paul I. Barton

Published in
Automatic Differentiation: Applications, Theory, and Implementations

Editor(s)
H. M. Bücker, G. Corliss, P. Hovland, U. Naumann, B. Norris

Year
2005

Publisher
Springer

Abstract
A targeted ad approach is presented to calculate directional second order derivatives of ODE/DAE embedded functionals accurately and efficiently. This advance enables us to tackle the solution of large scale dynamic optimization problems using a truncated-Newton method where the Newton equation is solved approximately to update the direction for the next optimization step. The proposed directional second order adjoint method (dSOA) provides accurate Hessian-vector products for this algorithm. The implementation of the ``dSOA powered″ truncated-Newton method for the solution of large scale dynamic optimization problems is showcased with an example.

Cross-References
Bucker2005ADA

AD Tools
TAMC

BibTeX
@INCOLLECTION{
         Ozyurt2005AoT,
       title = "Application of Targeted Automatic Differentiation to Large-Scale Dynamic
         Optimization",
       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",
       year = "2005",
       author = "Derya B. {\"O}zyurt and Paul I. Barton",
       abstract = "A targeted AD approach is presented to calculate directional second order
         derivatives of ODE/DAE embedded functionals accurately and efficiently. This advance enables us to
         tackle the solution of large scale dynamic optimization problems using a truncated-Newton method
         where the Newton equation is solved approximately to update the direction for the next optimization
         step. The proposed directional second order adjoint method (dSOA) provides accurate Hessian-vector
         products for this algorithm. The implementation of the ``dSOA powered'' truncated-Newton
         method for the solution of large scale dynamic optimization problems is showcased with an example.",
       crossref = "Bucker2005ADA",
       ad_area = "Differential-Algebraic Equation, Dynamic Optimization",
       ad_tools = "TAMC",
       pages = "235--247",
       doi = "10.1007/3-540-28438-9_21"
}


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