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All issues Volume 20 / No 1 (2024) J. Eur. Opt. Society-Rapid Publ., 20 1 (2024) 2 References
Open Access
Issue
J. Eur. Opt. Society-Rapid Publ.
Volume 20, Number 1, 2024
Article Number 2
Number of page(s) 11
DOI https://doi.org/10.1051/jeos/2023043
Published online 06 February 2024
  1. Rolland J.P., Davies M.A., Suleski T.J., Evans C., Bauer A., Lambropoulos J.C., Falaggis K. (2021) Freeform optics for imaging, Optica 8, 2, 161–176. [NASA ADS] [CrossRef] [Google Scholar]
  2. Schiesser E.M., Bauer A., Rolland J.P. (2019) Effect of freeform surfaces on the volume and performance of unobscured three mirror imagers in comparison with off-axis rotationally symmetric polynomials, Opt. Express 27, 15, 21750–21765. https://doi.org/10.1364/OE.27.021750. [NASA ADS] [CrossRef] [Google Scholar]
  3. Forbes G.W. (2012) Characterizing the shape of freeform optics, Opt. Express 20, 3, 2483–2499. https://doi.org/10.1364/OE.20.002483. [NASA ADS] [CrossRef] [Google Scholar]
  4. Chrisp M.P. (2014) New freeform NURBS imaging design code, in: International Optical Design Conference, Optical Society of America, paper ITh3A-7. [Google Scholar]
  5. FANO: fast accurate Nurbs optimization. Available at https://sc22.mghpcc.org/project/fast-accurate-nurbs-optimization-fano/. [Google Scholar]
  6. Volatier J.B., Menduiña-Fernández Á., Erhard M. (2017) Generalization of differential ray tracing by automatic differentiation of computational graphs, J. Opt. Soc. Am. 34, 7, 1146–1151. https://doi.org/10.1364/JOSAA.34.001146. [NASA ADS] [CrossRef] [Google Scholar]
  7. Chrisp M.P., Primeau B., Echter M.A. (2016) Imaging freeform optical systems designed with NURBS surfaces, Opt. Eng. 55, 7, 071208. https://doi.org/10.1117/1.OE.55.7.071208. [NASA ADS] [CrossRef] [Google Scholar]
  8. Abert O.P. (2005) Interactive ray tracing of NURBS surfaces by using SIMD instructions and the GPU in parallel. Diploma Thesis, Nanyang Technological University. Available at https://userpages.uni-koblenz.de/~cg/Diplomarbeiten/DA_Oliver_Abert.pdf. [Google Scholar]
  9. Baydin A.G., Pearlmutter B.A., Radul A.A., Siskind J.M. (2017) Automatic differentiation in machine learning: a survey, J. Mach. Learn. Res. 18, 5595–5637. [Google Scholar]
  10. Revels J., Lubin M., Papamarkou T. (2016) Forward-mode automatic differentiation in Julia. Available at https://arxiv.org/abs/1607.07892 (visited on 07.08.2019). [Google Scholar]
  11. Bezanson J., Karpinski S., Shah V.B., Edelman A. (2012) Julia: A fast dynamic language for technical computing. Available at https://arxiv.org/abs/1209.5145. [Google Scholar]
  12. Schittkowski K. (1988) Solving constrained nonlinear least squares problems by a general purpose SQP-method, in: Trends in Mathematical Optimization: 4th French–German Conference on Optimization, Springer, pp. 295–309. [CrossRef] [Google Scholar]
  13. Moré J.J. (1978) The Levenberg–Marquardt algorithm: implementation and theory, in: Watson G.A. (ed), Numerical Analysis, Springer, Berlin Heidelberg, pp. 105–116. ISBN: 978-3-540-35972-2. [Google Scholar]
  14. MATLAB (2010) Version 7.10.0 (R2010a), The MathWorks Inc., Natick, Massachusetts. [Google Scholar]
  15. Van Rossum G., Drake F.L. (2009) Python 3 reference manual, CreateSpace, Scotts Valley, CA. ISBN: 1441412697. [Google Scholar]
  16. Lam S.K., Pitrou A., Seibert S. (2015) Numba: A LLVM-based python JIT compiler, in: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC (LLVM ‘15), Association for Computing Machinery, New York, NY, pp. 1–6. https://doi.org/10.1145/2833157.2833162. [Google Scholar]
  17. Arakaki T., Bolewski J., Deits R., Fischer K., Johnson S.G., Bussonnier M., Norton I., Haraldsson P., Rocklin M., Shah V.B., Soto D. (2020) JuliaPy/pyjulia: PyJulia v0.5.6. Version v0.5.6. https://doi.org/10.5281/zenodo.4294940. [Google Scholar]
  18. Christ S., Schwabeneder D., Rackauckas C., Borregaard M.K., Breloff T. (2023) Plots.jl – a user extendable plotting API for the julia programming language, J. Open Res. Soft. 11, 1, 5. https://doi.org/10.5334/jors.431. [CrossRef] [Google Scholar]
  19. MeshCat.jl. Available at https://github.com/rdeits/MeshCat.jl (visited on 11.20.2023). [Google Scholar]
  20. Nonconvex.jl. Available at https://github.com/JuliaNonconvex/Nonconvex.jl (visited on 11.20.2023). [Google Scholar]
  21. Virtanen P., Gommers R., Oliphant T.E., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J., Van der Walt S.J. (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods 17, 3, 261–272. https://doi.org/10.1038/s41592-019-0686-2. [NASA ADS] [CrossRef] [Google Scholar]
  22. Formidable. Available at https://gitlab.space-codev.org/formidable/formidable (visited on 11.20.2023). [Google Scholar]
  23. ESCL. Available at https://essr.esa.int/license/european-space-agency-communitylicense-v2-4-strong-copyleft-type-1 (visited on 11.20.2023). [Google Scholar]

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