2006 | OriginalPaper | Chapter
Application of Targeted Automatic Differentiation to Large-Scale Dynamic Optimization
Authors : Derya B. Özyurt, Paul I. Barton
Published in: Automatic Differentiation: Applications, Theory, and Implementations
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.