Automatic Differentiation: Applications, Theory, and Implementations

TitleAutomatic Differentiation: Applications, Theory, and Implementations
Publication TypeBook Chapter
Year of Publication2006
AuthorsÖzyurt DB, Barton PI
EditorH Bücker M, Corliss GF, Hovland P, Naumann U, Norris B
Book TitleLecture Notes in Computational Science and Engineering
Volume50
ChapterApplication of Targeted Automatic Differentiation to Large Scale Dynamic Optimization
Pagination235-248
PublisherSpringer
CityNew 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.

URLhttp://www.springerlink.com/content/k1266x55385213kq/?p=12b2415581974bae961b02e18675681e&pi=0
DOI10.1007/3-540-28438-9_21