Algorithm 799: revolve. An implementation of checkpointing for the reverse or adjoint mode of computational differentiation. This is an excellent paper, describing a variant (“revolve”) of the basic form for reverse differentiation for computing the gradient of a scalar valued function, which enables computing this gradient of a function using no more than five times the number of operations needed for evaluating the function. This basic algorithm usually requires a large memory for storage of intermediate computations. The variant presented here circumvents this large memory requirement. A detailed description of the variant is given, along with motivation and proofs. The authors then illustrate the application of their algorithm to the solution of Burgers equation (Source:

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 59 articles , 1 standard article )

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  1. Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
  2. Charpentier, Isabelle; Cochelin, Bruno: Towards a full higher order AD-based continuation and bifurcation framework (2018)
  3. Liu, Jun; Wang, Zhu: Efficient time domain decomposition algorithms for parabolic PDE-constrained optimization problems (2018)
  4. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  5. Schmidt, Stephan: Weak and strong form shape hessians and their automatic generation (2018)
  6. Towara, M.; Naumann, U.: SIMPLE adjoint message passing (2018)
  7. Yang, Pengliang; Brossier, Romain; Métivier, Ludovic; Virieux, Jean; Zhou, Wei: A time-domain preconditioned truncated Newton approach to visco-acoustic multiparameter full waveform inversion (2018)
  8. Aupy, Guillaume; Herrmann, Julien: Periodicity in optimal hierarchical checkpointing schemes for adjoint computations (2017)
  9. Hückelheim, Jan Christian; Hascoët, Laurent; Müller, Jens-Dominik: Algorithmic differentiation of code with multiple context-specific activities (2017)
  10. Plessix, René-Édouard: Some computational aspects of the time and frequency domain formulations of seismic waveform inversion (2017)
  11. Aupy, Guillaume; Herrmann, Julien; Hovland, Paul; Robert, Yves: Optimal multistage algorithm for adjoint computation (2016)
  12. Barker, Andrew T.; Rees, Tyrone; Stoll, Martin: A fast solver for an H1 regularized PDE-constrained optimization problem (2016)
  13. Bauman, Paul T.; Stogner, Roy H.: GRINS: a multiphysics framework based on the libMesh finite element library (2016) ioport
  14. Hascoët, Laurent; Utke, Jean: Programming language features, usage patterns, and the efficiency of generated adjoint code (2016)
  15. Li, Y.; Han, Bo; Métivier, L.; Brossier, R.: Optimal fourth-order staggered-grid finite-difference scheme for 3D frequency-domain viscoelastic wave modeling (2016)
  16. Nørgaard, Sebastian; Sigmund, Ole; Lazarov, Boyan: Topology optimization of unsteady flow problems using the lattice Boltzmann method (2016)
  17. Papoutsis-Kiachagias, E. M.; Giannakoglou, K. C.: Continuous adjoint methods for turbulent flows, applied to shape and topology optimization: industrial applications (2016)
  18. Sluşanschi, Emil I.; Dumitrel, Vlad: ADiJaC -- automatic differentiation of Java classfiles (2016)
  19. Bakhos, Tania; Saibaba, Arvind K.; Kitanidis, Peter K.: A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers (2015)
  20. Bockelmann, Hendryk; Barz, Dominik P. J.: Optimised active flow control for micromixers and other fluid applications: sensitivity- vs. adjoint-based strategies (2015)

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