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9  References

[aut05]

autodiff.org. List of automatic differentiation tools. http://www.autodiff.org/?module=Tools, 2005.

[Azm97]

Yousry Y. Azmy. Post-convergence automatic differentiation of iterative schemes. Nuclear Science and Engineering, 125:12–18, 1997.

[BB98a]

M.C. Bartholomew-Biggs. Using forward accumulation for automatic differentiation of implictly-defined functions. Computational Optimization and Applications, 9:65–84, 1998.

[BB98b]

Michael C. Bartholomew-Biggs. Using forward accumulation for automatic differentiation of implicitly-defined functions. Computational Optimization and Applications, 9:65–84, 1998.

[BCH⁺98]

Christian H. Bischof, Alan Carle, Paul Hovland, Peyvand Khademi, and Andrew Mauer. ADIFOR 2.0 users' guide (revision D). Technical Report ANL/MCS-P263-0991, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439 USA, 1998. Available via http://www.mcs.anl.gov/adifor/.

[BCH⁺05]

H. Martin Bücker, George F. Corliss, Paul D. Hovland, Uwe Naumann, and Boyana Norris, editors. Automatic Differentiation: Applications, Theory, and Implementations, volume 50 of Lecture Notes in Computational Science and Engineering. Springer, New York, NY, 2005.

[BCKM96]

Christian H. Bischof, Alan Carle, Peyvand Khademi, and Andrew Mauer. ADIFOR 2.0: Automatic differentiation of Fortran 77 programs. IEEE Computational Science & Engineering, 3(3):18–32, 1996.

[BRM97]

Christian H. Bischof, Lucas Roh, and Andrew Mauer. ADIC — An extensible automatic differentiation tool for ANSI-C. Software–Practice and Experience, 27(12):1427–1456, 1997.

[Chr94]

Bruce Christianson. Reverse accumulation and attractive fixed points. Optimization Methods and Software, 3:311–326, 1994.

[Chr98]

Bruce Christianson. Reverse accumulation and implicit functions. Optimization Methods and Software, 9(4):307–322, 1998.

[CV96]

Thomas F. Coleman and Arun Verma. Structure and efficient Jacobian calculation. In Martin Berz, Christian H. Bischof, George F. Corliss, and Andreas Griewank, editors, Computational Differentiation: Techniques, Applications, and Tools, chapter 13, pages 149–159. SIAM, Philadelphia, PA, 1996.

[CV00]

Thomas F. Coleman and Arun Verma. ADMIT-1: Automatic differentiation and MATLAB interface toolbox. ACM Transactions on Mathematical Software, 26(1):150–175, 2000.

[Dor96]

John R. Dormand. Numerical Methods for Differential Equations. Library of Engineering Mathematics. CRC Press, 1996.

[DS96]

J.E. Dennis, Jr. and Robert B. Schnabel. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. SIAM Classice in Applied Mathematics. SIAM, Philadelphia, 1996.

[FK04]

Shaun A. Forth and Robert Ketzscher. High-level interfaces for the MAD (Matlab Automatic Differentiation) package. In P. Neittaanmäki, T. Rossi, S. Korotov, E. Oñate, J. Périaux, and D. Knörzer, editors, 4th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS), volume 2. University of Jyväskylä, Department of Mathematical Information Technology, Finland, Jul 24–28 2004. ISBN 951-39-1869-6.

[For01]

Shaun A. Forth. Notes on differentiating determinants. Technical Report AMOR 2001/1, Applied Mathematics & Operational Research, Cranfield University (RMCS Shrivenham), 2001.

[For06]

Shaun A. Forth. An efficient overloaded implementation of forward mode automatic differentiation in MATLAB. ACM Transactions on Mathematical Software, 32(2), June 2006.

[GJU96]

Andreas Griewank, David Juedes, and Jean Utke. Algorithm 755: ADOL-C: A package for the automatic differentiation of algorithms written in CC++. ACM Transactions on Mathematical Software, 22(2):131–167, 1996.

[GK96]

Ralf Giering and Thomas Kaminski. Recipes for adjoint code construction. Technical Report 212, Max-Planck-Institut für Meteorologie, Hamburg, Germany, 1996.

[GMS91]

John R. Gilbert, Cleve Moler, and Robert Schreiber. Sparse matrices in MATLAB: Design and implementation. Technical paper, The Mathworks, 1991. Available via www.mathworks.com.

[Gri00]

Andreas Griewank. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Number 19 in Frontiers in Applied Mathematics. SIAM, Philadelphia, PA, 2000.

[HS98]

A.K.M. Shahadat Hossain and Trond Steihaug. Computing a sparse Jacobian matrix by rows and columns. Optimization Methods and Software, 10:33–48, 1998.

[INR05]

INRIA Tropics Project. TAPENADE 2.0. http://www-sop.inria.fr/tropics, 2005.

[Kub94]

K. Kubota. Matrix inversion algorithms by means of automatic differentiation. Applied Mathematics Letters, 7(4):19–22, 1994.

[mat06a]

The MathWorks Inc., 3 Apple Hill Drive, Natick MA 01760-2098. MATLAB Mathematics, March 2006. http://www.mathworks.com/access/helpdesk/help/pdf_doc/matlab/math.pdf.

[Mat06b]

The Mathworks Inc., 3 Apple Hill Drive, Natick MA 01760-2098. Optimization Toolbox User's Guide, 2006. http://www.mathworks.com/access/helpdesk/help/pdf_doc/optim/optim_tb.pdf.

[NW99]

Jorge Nocedal and Stephen J. Wright. Numerical Optimization. Springer series in operational research. Springer-Verlag, New York, 1999.

[PR98]

J.D. Pryce and J.K. Reid. ADO1, a Fortran 90 code for automatic differentiation. Technical Report RAL-TR-1998-057, Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire, OX11 OQX, England, 1998. Available via ftp://matisa.cc.rl.ac.uk/pub/reports/ prRAL98057.ps.gz.

[RH92]

Lawrence C. Rich and David R. Hill. Automatic differentiation in MATLAB. App. Num. Math., 9:33–43, 1992.

[SR97]

L.F. Shampine and M.W. Reichelt. The MATLAB ODE suite. SIAM J. Sci. Comput., 18:1–22, 1997.

[Ver98a]

Arun Verma. ADMAT: Automatic differentiation in MATLAB using object oriented methods. In SIAM Interdisciplinary Workshop on Object Oriented Methods for Interoperability, pages 174–183, Yorktown Heights, New York, Oct 21-23 1998. SIAM, National Science Foundation.

[Ver98b]

Arun Verma. Structured Automatic Differentiation. PhD thesis, Department of Computer Science, Cornell University, Ithaca, NY, 1998.

[Ver98c]

Arun Verma. Structured Automatic Differentiation. PhD thesis, Cornell University, 1998.

[Ver99]

Arun Verma. ADMAT: Automatic differentiation in MATLAB using object oriented methods. In M. E. Henderson, C. R. Anderson, and S. L. Lyons, editors, Object Oriented Methods for Interoperable Scientific and Engineering Computing: Proceedings of the 1998 SIAM Workshop, pages 174–183, Philadelphia, 1999. SIAM.

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