revolve
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: http://dl.acm.org/)
This software is also peer reviewed by journal TOMS.
This software is also peer reviewed by journal TOMS.
Keywords for this software
References in zbMATH (referenced in 68 articles , 1 standard article )
Showing results 1 to 20 of 68.
Sorted by year (- Eggl, M. F.; Schmid, Peter J.: Mixing enhancement in binary fluids using optimised stirring strategies (2020)
- Herrmann, Julien; Pallez, Guillaume (Aupy): H-revolve: a framework for adjoint computation on synchronous hierarchical platforms (2020)
- 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)
- Farrell, P. E.; Hake, J. E.; Funke, S. W.; Rognes, M. E.: Automated adjoints of coupled PDE-ODE systems (2019)
- Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
- Naumann, Uwe: Adjoint code design patterns (2019)
- Tropp, Joel A.; Yurtsever, Alp; Udell, Madeleine; Cevher, Volkan: Streaming low-rank matrix approximation with an application to scientific simulation (2019)
- Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
- Charpentier, Isabelle; Cochelin, Bruno: Towards a full higher order AD-based continuation and bifurcation framework (2018)
- Dilgen, Cetin B.; Dilgen, Sumer B.; Fuhrman, David R.; Sigmund, Ole; Lazarov, Boyan S.: Topology optimization of turbulent flows (2018)
- Liu, Jun; Wang, Zhu: Efficient time domain decomposition algorithms for parabolic PDE-constrained optimization problems (2018)
- Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
- Schmidt, Stephan: Weak and strong form shape hessians and their automatic generation (2018)
- Towara, M.; Naumann, U.: SIMPLE adjoint message passing (2018)
- 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)
- Aupy, Guillaume; Herrmann, Julien: Periodicity in optimal hierarchical checkpointing schemes for adjoint computations (2017)
- Hückelheim, Jan Christian; Hascoët, Laurent; Müller, Jens-Dominik: Algorithmic differentiation of code with multiple context-specific activities (2017)
- Plessix, René-Édouard: Some computational aspects of the time and frequency domain formulations of seismic waveform inversion (2017)
- Aupy, Guillaume; Herrmann, Julien; Hovland, Paul; Robert, Yves: Optimal multistage algorithm for adjoint computation (2016)
- Barker, Andrew T.; Rees, Tyrone; Stoll, Martin: A fast solver for an H1 regularized PDE-constrained optimization problem (2016)