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Merge pull request #671 from PyAutoLabs/docs/cite-prodigy
docs: cite Prodigy separately from Optax
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docs/general/citations.md

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@@ -28,6 +28,28 @@ interferometer non-uniform FFT (`nufftax` and its FINUFFT algorithm, keys `nufft
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`finufft`, see below), and the critical-curve/caustic solver (`Jax-Zero-Contour`, key
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`jax_zero_contour`, see below).
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If you use the learning-rate-free `af.MultiStartProdigy` search, also cite the Prodigy
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method under the key `prodigy`. It is a published algorithm in its own right, run through
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Optax's implementation (`optax.contrib.prodigy`), so cite it alongside `optax` rather than
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in place of it:
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```bibtex
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@inproceedings{prodigy,
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author = {Mishchenko, Konstantin and Defazio, Aaron},
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title = {{Prodigy: An Expeditiously Adaptive Parameter-Free Learner}},
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booktitle = {Proceedings of the 41st International Conference on Machine Learning},
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series = {Proceedings of Machine Learning Research},
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volume = {235},
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pages = {35779--35804},
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publisher = {PMLR},
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url = {https://proceedings.mlr.press/v235/mishchenko24a.html},
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year = {2024}
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}
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```
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The reference implementation is at <https://github.com/konstmish/prodigy> and the preprint
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at <https://arxiv.org/abs/2306.06101>.
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## Jax-Zero-Contour
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If you use the zero-contour method for critical curve and caustic computation (the default in

files/citations.bib

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year = {2020}
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}
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@inproceedings{prodigy,
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author = {Mishchenko, Konstantin and Defazio, Aaron},
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title = {{Prodigy: An Expeditiously Adaptive Parameter-Free Learner}},
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booktitle = {Proceedings of the 41st International Conference on Machine Learning},
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series = {Proceedings of Machine Learning Research},
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volume = {235},
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pages = {35779--35804},
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publisher = {PMLR},
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url = {https://proceedings.mlr.press/v235/mishchenko24a.html},
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year = {2024}
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}
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@article{anesthetic,
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author = {Handley, Will},
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title = {anesthetic: nested sampling visualisation},

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