Geodesic
Efficient calculation of sensitivities for optimization problems
Discussiones Mathematicae. Differential Inclusions, Control and Optimization, Tome 27 (2007) no. 1, pp. 119-134.

Voir la notice de l'article provenant de la source Library of Science

Sensitivity information is required by numerous applications such as, for example, optimization algorithms, parameter estimations or real time control. Sensitivities can be computed with working accuracy using the forward mode of automatic differentiation (AD).
Keywords: automatic differentiation, sensitivities, forward mode 
@article{DMDICO_2007_27_1_a6,
     author = {Kowarz, Andreas and Walther, Andrea},
     title = {Efficient calculation of sensitivities for optimization problems},
     journal = {Discussiones Mathematicae. Differential Inclusions, Control and Optimization},
     pages = {119--134},
     publisher = {mathdoc},
     volume = {27},
     number = {1},
     year = {2007},
     zbl = {1152.65417},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/DMDICO_2007_27_1_a6/}
}
TY  - JOUR
AU  - Kowarz, Andreas
AU  - Walther, Andrea
TI  - Efficient calculation of sensitivities for optimization problems
JO  - Discussiones Mathematicae. Differential Inclusions, Control and Optimization
PY  - 2007
SP  - 119
EP  - 134
VL  - 27
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/DMDICO_2007_27_1_a6/
LA  - en
ID  - DMDICO_2007_27_1_a6
ER  - 
%0 Journal Article
%A Kowarz, Andreas
%A Walther, Andrea
%T Efficient calculation of sensitivities for optimization problems
%J Discussiones Mathematicae. Differential Inclusions, Control and Optimization
%D 2007
%P 119-134
%V 27
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/DMDICO_2007_27_1_a6/
%G en
%F DMDICO_2007_27_1_a6
Kowarz, Andreas; Walther, Andrea. Efficient calculation of sensitivities for optimization problems. Discussiones Mathematicae. Differential Inclusions, Control and Optimization, Tome 27 (2007) no. 1, pp. 119-134. http://geodesic.mathdoc.fr/item/DMDICO_2007_27_1_a6/

[1] M.B. Bell, CppAD: A package for C++ Algorithmic Differentiation, COIN-OR foundation, Available at http://www.coin-or.org/CppAD

[2] C. Bendtsen and O. Stauning, FADBAD, a flexible C++ package for automatic differentiation, Department of Mathematical Modelling, Technical University of Denmark, 1996, Available at http://www.imm.dtu.dk/fadbad.html

[3] J. Dongarra, S. Moore, P. Mucci, K. Seymour, D. Terpstra, Q. Xia and H. You, Performance Application Programming Interface, Innovative Computing Laboratory, Department of Computer Science, University of Tennessee, Available at http://icl.cs.utk.edu/papi

[4] R. Giering and T. Kaminski, Recipes for Adjoint Code Construction, ACM Trans. Math. Software 24 (1998), 437-474. URL: http://www.fastopt.com

[5] A. Griewank, A. Kowarz and A. Walther, Documentation of ADOL-C, Available at http://www.math.tu-dresden.de/~adol-c, Updated Version of: A. Griewank, D. Juedes, J. Utke J, ADOL-C: A package for the automatic differentiation of algorithms written in C/C++. ACM Trans. Math. Software 22 (1996), 131-167.

[6] A. Griewank A, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Number 19 in Frontiers in Appl. Math. SIAM, Philadelphia, 2000.

[7] R. Griesse and A. Walther, Parametric sensitivities for optimal control problems using automatic differentiation, Optimal Control Applications and Methods 24 (2003), 297-314.

[8] L. Hascoët and V. Pascual, Tapenade 2.1 user's guide. Tech. rep. 300, INRIA, 2004.

[9] F. Mazzia F and F. Iavernaro, Test Set for Initial Value Problem Solvers, Entire test set description, Department of Mathematics, University of Bari, August 2003, Available at http://www.pitagora.dm.uniba.it/~testset

[10] M. Knauer and C. Büskens, Real-Time Trajectory Planning of the Industrial Robot IRB6400, PAMM. 3 (2003), 515-516.