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.
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© 2006 Springer
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Özyurt, D.B., Barton, P.I. (2006). Application of Targeted Automatic Differentiation to Large-Scale Dynamic Optimization. In: Bücker, M., Corliss, G., Naumann, U., Hovland, P., Norris, B. (eds) Automatic Differentiation: Applications, Theory, and Implementations. Lecture Notes in Computational Science and Engineering, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28438-9_21
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DOI: https://doi.org/10.1007/3-540-28438-9_21
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28403-1
Online ISBN: 978-3-540-28438-3
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