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 70 articles , 1 standard article )

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  1. Givoli, Dan: A tutorial on the adjoint method for inverse problems (2021)
  2. Muthukumar, Ramchandran; Kouri, Drew P.; Udell, Madeleine: Randomized sketching algorithms for low-memory dynamic optimization (2021)
  3. Eggl, M. F.; Schmid, Peter J.: Mixing enhancement in binary fluids using optimised stirring strategies (2020)
  4. Herrmann, Julien; Pallez, Guillaume (Aupy): H-revolve: a framework for adjoint computation on synchronous hierarchical platforms (2020)
  5. Rutkowski, Mariusz; Gryglas, Wojciech; Szumbarski, Jacek; Leonardi, Christopher; Łaniewski-Wołłk, Łukasz: Open-loop optimal control of a flapping wing using an adjoint lattice Boltzmann method (2020)
  6. Farrell, P. E.; Hake, J. E.; Funke, S. W.; Rognes, M. E.: Automated adjoints of coupled PDE-ODE systems (2019)
  7. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  8. Naumann, Uwe: Adjoint code design patterns (2019)
  9. Tropp, Joel A.; Yurtsever, Alp; Udell, Madeleine; Cevher, Volkan: Streaming low-rank matrix approximation with an application to scientific simulation (2019)
  10. Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
  11. Charpentier, Isabelle; Cochelin, Bruno: Towards a full higher order AD-based continuation and bifurcation framework (2018)
  12. Dilgen, Cetin B.; Dilgen, Sumer B.; Fuhrman, David R.; Sigmund, Ole; Lazarov, Boyan S.: Topology optimization of turbulent flows (2018)
  13. Liu, Jun; Wang, Zhu: Efficient time domain decomposition algorithms for parabolic PDE-constrained optimization problems (2018)
  14. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  15. Schmidt, Stephan: Weak and strong form shape hessians and their automatic generation (2018)
  16. Towara, M.; Naumann, U.: SIMPLE adjoint message passing (2018)
  17. 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)
  18. Aupy, Guillaume; Herrmann, Julien: Periodicity in optimal hierarchical checkpointing schemes for adjoint computations (2017)
  19. Hückelheim, Jan Christian; Hascoët, Laurent; Müller, Jens-Dominik: Algorithmic differentiation of code with multiple context-specific activities (2017)
  20. Plessix, René-Édouard: Some computational aspects of the time and frequency domain formulations of seismic waveform inversion (2017)

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