The art of differentiating computer programs. An introduction to algorithmic differentiation. This is the first entry-level book on algorithmic (also known as automatic) differentiation (AD), providing fundamental rules for the generation of first- and higher-order tangent-linear and adjoint code. The author covers the mathematical underpinnings as well as how to apply these observations to real-world numerical simulation programs.par Readers will find: * many examples and exercises, including hints to solutions; * the prototype AD tools dco and dcc for use with the examples and exercises; * first- and higher-order tangent-linear and adjoint modes for a limited subset of C/C++, provided by the derivative code compiler dcc.; * a supplementary website containing sources of all software discussed in the book, additional exercises and comments on their solutions (growing over the coming years), links to other sites on AD, and errata.par Audience: This book is intended for undergraduate and graduate students in computational science, engineering, and finance as well as applied mathematics and computer science. It will provide researchers and developers at all levels with an intuitive introduction to AD.

References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Charpentier, Isabelle: Optimized higher-order automatic differentiation for the Faddeeva function (2016)
  2. Evtushenko, Yu. G.; Zubov, V. I.: Generalized fast automatic differentiation technique (2016)
  3. Gower, R. M.; Gower, A. L.: Higher-order reverse automatic differentiation with emphasis on the third-order (2016)
  4. Günther, Stefanie; Gauger, Nicolas R.; Wang, Qiqi: Simultaneous single-step one-shot optimization with unsteady PDEs (2016)
  5. Wang, Mu; Gebremedhin, Assefaw; Pothen, Alex: Capitalizing on \itlive variables: new algorithms for efficient Hessian computation via automatic differentiation (2016)
  6. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  7. Hannemann-Tamás, Ralf; Muñoz, Diego A.; Marquardt, Wolfgang: Adjoint sensitivity analysis for nonsmooth differential-algebraic equation systems (2015)
  8. Houska, Boris; Villanueva, Mario E.; Chachuat, Beno^ıt: Stable set-valued integration of nonlinear dynamic systems using affine set-parameterizations (2015)
  9. Naumann, Uwe; Lotz, Johannes; Leppkes, Klaus; Towara, Markus: Algorithmic differentiation of numerical methods: tangent and adjoint solvers for parameterized systems of nonlinear equations (2015)
  10. Rothe, Steffen; Hartmann, Stefan: Automatic differentiation for stress and consistent tangent computation (2015) ioport
  11. Springer, Julia; Urban, Karsten: Adjoint-based optimization for rigid body motion in multiphase Navier-Stokes flow (2015)
  12. Bosse, Torsten; Lehmann, Lutz; Griewank, Andreas: Adaptive sequencing of primal, dual, and design steps in simulation based optimization (2014)
  13. Farrell, P. E.; Ham, D. A.; Funke, S. W.; Rognes, M. E.: Automated derivation of the adjoint of high-level transient finite element programs (2013)
  14. Hannemann-Tamás, Ralf; Gábor, Attila; Szederkényi, Gábor; Hangos, Katalin M.: Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming (2013)
  15. Naumann, Uwe: The art of differentiating computer programs. An introduction to algorithmic differentiation. (2012)