Paul D Hovland et al 2005 J. Phys.: Conf. Ser. 16 466 doi:10.1088/1742-6596/16/1/063
Paul D Hovland1, Boyana Norris1, Michelle Mills Strout1, Sanjukta Bhowmick1,2 and Jean Utke3
Show affiliationsAutomatic differentiation is a technique for transforming a program or subprogram that computes a function, including arbitrarily complex simulation codes, into one that computes the derivatives of that function. We describe the implementation and application of automatic differentiation tools. We highlight recent advances in the combinatorial algorithms and compiler technology that underlie successful implementation of automatic differentiation tools. We discuss applications of automatic differentiation in design optimization and sensitivity analysis. We also describe ongoing research in the design of language-independent source transformation infrastructures for automatic differentiation algorithms.
02.60.Jh Numerical differentiation and integration
Issue 1 (2005)
Paul D Hovland et al 2005 J. Phys.: Conf. Ser. 16 466
C K Ross and N V Klassen 2009 Phys. Med. Biol. 54 L11
R Loll 1998 Class. Quantum Grav. 15 799
A E Hoetink et al 2002 Physiol. Meas. 23 457
L.H.T. Rietjens 1979 Physics in Technology 10 216
M Ghali et al 2004 Semicond. Sci. Technol. 19 359
Beatriz Millan Malo et al 2001 J. Phys.: Condens. Matter 13 1361
R Golestanian et al 1995 Class. Quantum Grav. 12 273
Laurent Freidel and Etera R Livine 2006 Class. Quantum Grav. 23 2021
D Grossman et al 2008 New J. Phys. 10 023036