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Efficient High-Order Methods for ODEs and DAEs-
incollection
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Author(s)
Jens Hoefkens
, Martin Berz
, Kyoko Makino
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Published in Automatic Differentiation of Algorithms: From Simulation to Optimization
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Editor(s) George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann |
Year 2002 |
Publisher Springer |
Abstract We present methods for the high-order differentiation through ordinary differential equations (ODEs), and more importantly, differential algebraic equations (DAEs). First, methods are developed that assert that the requested derivatives are really those of the solution of the ODE, and not those of the algorithm used to solve the ODE. Next, high-order solvers for DAEs are developed that in a fully automatic way turn an n-th order solution step of the DAEs into a corresponding step for an ODE initial value problem. In particular, this requires the automatic high-order solution of implicit relations, which is achieved using an iterative algorithm that converges to the exact result in at most n+1 steps. We give examples of the performance of the method. |
Cross-References Corliss2002ADo |
BibTeX
@INCOLLECTION{
Hoefkens2002EHO,
author = "Jens Hoefkens and Martin Berz and Kyoko Makino",
title = "Efficient High-Order Methods for {ODE}s and {DAE}s",
pages = "343--348",
abstract = "We present methods for the high-order differentiation through ordinary differential
equations (ODEs), and more importantly, differential algebraic equations (DAEs). First, methods are
developed that assert that the requested derivatives are really those of the solution of the ODE,
and not those of the algorithm used to solve the ODE. Next, high-order solvers for DAEs are
developed that in a fully automatic way turn an $n$-th order solution step of the DAEs into a
corresponding step for an ODE initial value problem. In particular, this requires the automatic
high-order solution of implicit relations, which is achieved using an iterative algorithm that
converges to the exact result in at most $n+1$ steps. We give examples of the performance of the
method.",
chapter = "41",
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"
}
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