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Data-flow reversal and garbage collection. (English) Zbl 07912540

MSC:

65-XX Numerical analysis
Full Text: DOI

References:

[1] Baydin, A., Pearlmutter, B., Radul, A., and Siskind, J.. 2018. Automatic differentiation in machine learning: A survey. J. Mach. Learn. Res.18 (2018), 1-43. · Zbl 06982909
[2] Berz, Martin, Bischof, Christian, Corliss, George, and Griewank, Andreas (Eds.). 1996. Computational Differentiation: Techniques, Applications and Tools. SIAM, Philadelphia, PA. · Zbl 0857.00033
[3] Bischof, Christian H., Bücker, H. Martin, Hovland, Paul D., Naumann, Uwe, and Utke, Jean (Eds.). 2008. Advances in Automatic Differentiation. , Vol. 64. Springer, Berlin. DOI: · Zbl 1143.65003
[4] Bradbury, James, Frostig, Roy, Hawkins, Peter, Johnson, Matthew James, Leary, Chris, Maclaurin, Dougal, Necula, George, Paszke, Adam, VanderPlas, Jake, Wanderman-Milne, Skye, and Zhang, Qiao. 2018. JAX: Composable Transformations of Python+NumPy Programs. Retrieved from http://github.com/google/jax
[5] Bücker, M., Corliss, G., Hovland, P., Naumann, U., and Norris, B.. 2005. Automatic Differentiation: Applications, Theory, and Tools. Springer.
[6] Christianson, Bruce, Forth, Shaun A., and Griewank, Andreas (Eds.). 2018. Special Issue of Optimization Methods & Software: Advances in Algorithmic Differentiation. Taylor & Francis, vol. 33. · Zbl 1397.00042
[7] Corliss, George, Faure, Christèle, Griewank, Andreas, Hascoët, Laurent, and Naumann, Uwe (Eds.). 2002. Automatic Differentiation of Algorithms: From Simulation to Optimization. Springer, New York, NY. DOI:
[8] Fagan, M., Hascoët, L., and Utke, J.. 2006. Data representation alternatives in semantically augmented numerical models. In 6th IEEE International Workshop on Source Code Analysis and Manipulation.
[9] Forth, Shaun, Hovland, Paul, Phipps, Eric, Utke, Jean, and Walther, Andrea (Eds.). 2012. Recent Advances in Algorithmic Differentiation. , Vol. 87. Springer, Berlin. DOI: · Zbl 1247.65002
[10] Griewank, Andreas and Corliss, George F. (Eds.). 1991. Automatic Differentiation of Algorithms: Theory, Implementation, and Application. SIAM, Philadelphia, PA. · Zbl 0747.00030
[11] Griewank, A. and Walther, A.. 2008. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (2nd ed.). Vol. 105. SIAM, Philadelphia, PA. Retrieved from http://www.ec-securehost.com/SIAM/OT105.html · Zbl 1159.65026
[12] Hascoët, L. and Morlighem, M.. 2017. Source-to-source adjoint algorithmic differentiation of an ice sheet model written in C. Optim. Meth. Softw. 33, 4-6 (2017), 829-843. DOI: · Zbl 1453.86048
[13] Hascoët, L., Naumann, U., and Pascual, V.. 2005. “To be recorded” analysis in reverse-mode automatic differentiation. Fut. Gen. Comput. Syst.21, 8 (2005), 1401-1417. DOI:
[14] Hascoët, Laurent and Utke, Jean. 2016. Programming language features, usage patterns, and the efficiency of generated adjoint code. Optim. Meth. Softw.31 (2016), 885-903. DOI: · Zbl 1365.65054
[15] Innes, M.. 2019. Don’t unroll adjoint: Differentiating SSA-form programs. Retrieved from https://fluxml.ai/Zygote.jl/latest/
[16] Naumann, U.. 2012. The Art of Differentiating Computer Programs: An Introduction to Algorithmic Differentiation. Vol. 24. SIAM, Philadelphia, PA. Retrieved from http://www.ec-securehost.com/SIAM/SE24.html · Zbl 1275.65015
[17] Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Demaison, A., Antiga, L., and Lerer, A.. 2017. Automatic differentiation in PyTorch. In NIPS 2017 Workshop Autodiff. Retrieved from https://pytorch.org/
[18] Sagebaum, M., Albring, T., and Gauger, N. R.. 2019. High-performance derivative computations using CoDiPack. ACM Trans. Math. Softw.45, 4, Article 38 (2019), 26 pages. DOI: · Zbl 1486.65029
[19] Slusanschi, Emil I. and Dumitrel, Vlad. 2016. ADiJaC—Automatic differentiation of Java classfiles. ACM Trans. Math. Softw.43, 2, Article 9 (Sep.2016), 33 pages. DOI: · Zbl 1391.65045
[20] Merriënboer, Bart van, Moldovan, Dan, and Wiltschko, Alex B.. 2018. Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming. In NeurIPS’18. Retrieved from: https://github.com/google/tangent
[21] Walther, A. and Griewank, A.. 2012. Getting started with ADOL-C. In Combinatorial Scientific Computing, Naumann, U. and Schenk, O. (Eds.). Chapman-Hall CRC Computational Science, 181-202.
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