Title | Automatic Differentiation: Applications, Theory, and Implementations |

Publication Type | Book Chapter |

Year of Publication | 2006 |

Authors | Özyurt DB, Barton PI |

Editor | H Bücker M, Corliss GF, Hovland P, Naumann U, Norris B |

Book Title | Lecture Notes in Computational Science and Engineering |

Volume | 50 |

Chapter | Application of Targeted Automatic Differentiation to Large Scale Dynamic Optimization |

Pagination | 235-248 |

Publisher | Springer |

City | New York |

Abstract | A targeted {AD} approach is presented to calculate directional second order derivatives of ODE/DAE embedded functionals accurately and eficiently. 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. |

URL | http://www.springerlink.com/content/k1266x55385213kq/?p=12b2415581974bae961b02e18675681e&pi=0 |

DOI | 10.1007/3-540-28438-9_21 |

# Automatic Differentiation: Applications, Theory, and Implementations

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