paper initialization

paper.bib

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+@inproceedings{kajiya1986rendering,
+  title={The rendering equation},
+  author={Kajiya, James T},
+  booktitle={Proceedings of the 13th annual conference on Computer graphics and interactive techniques},
+  pages={143--150},
+  year={1986}
+}
+
+@incollection{kollig2002efficient,
+  title={Efficient bidirectional path tracing by randomized quasi-Monte Carlo integration},
+  author={Kollig, Thomas and Keller, Alexander},
+  booktitle={Monte Carlo and Quasi-Monte Carlo Methods 2000},
+  pages={290--305},
+  year={2002},
+  publisher={Springer},
+  doi={10.1007/978-3-642-56046-0_19}
+}
+
+@article{delbracio2014boosting,
+  title={Boosting monte carlo rendering by ray histogram fusion},
+  author={Delbracio, Mauricio and Mus{\'e}, Pablo and Buades, Antoni and Chauvier, Julien and Phelps, Nicholas and Morel, Jean-Michel},
+  journal={ACM Transactions on Graphics (TOG)},
+  volume={33},
+  number={1},
+  pages={1--15},
+  year={2014},
+  publisher={ACM New York, NY, USA},
+  doi={10.1145/2532708}
+}
+
+@inproceedings{boughida2017bayesian,
+  title={Bayesian collaborative denoising for Monte Carlo rendering},
+  author={Boughida, Malik and Boubekeur, Tamy},
+  booktitle={Computer Graphics Forum},
+  volume={36},
+  number={4},
+  pages={137--153},
+  year={2017},
+  organization={Wiley Online Library},
+  doi={10.1111/cgf.13231}
+}
+
+@book{pharr2016physically,
+  title={Physically based rendering: From theory to implementation},
+  author={Pharr, Matt and Jakob, Wenzel and Humphreys, Greg},
+  year={2016},
+  publisher={Morgan Kaufmann}
+}
+
+@inproceedings{xie2012image,
+  title={Image denoising and inpainting with deep neural networks},
+  author={Xie, Junyuan and Xu, Linli and Chen, Enhong},
+  booktitle={Advances in neural information processing systems},
+  pages={341--349},
+  year={2012}
+}
+
+@article{chaitanya2017interactive,
+  title={Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder},
+  author={Chaitanya, Chakravarty R Alla and Kaplanyan, Anton S and Schied, Christoph and Salvi, Marco and Lefohn, Aaron and Nowrouzezahrai, Derek and Aila, Timo},
+  journal={ACM Transactions on Graphics (TOG)},
+  volume={36},
+  number={4},
+  pages={1--12},
+  year={2017},
+  publisher={ACM New York, NY, USA},
+  doi={10.1145/3072959.3073601}
+}
+
+@misc{pbrtp3d,
+  author = {​PrISE-3D},
+  title = {PrISE-3D customized pbrt-v3},
+  year = {2020},
+  publisher = {​GitHub},
+  journal = {​GitHub repository},
+  url = {https://github.com/prise-3d/pbrt-v3}
+}

paper.md

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+---
+title: 'Minimalist And Customizable Optimization Package'
+tags:
+  - Python
+  - Operations Research
+  - Multi-objective
+authors:
+  - name: Jérôme BUISINE
+    orcid: 0000-0001-6071-744X
+    affiliation: 1 # (Multiple affiliations must be quoted)
+affiliations:
+ - name: Univ. Littoral Côte d’Opale, LISIC Calais, France, F-62100
+   index: 1
+date: 9 September 2020
+bibliography: paper.bib
+
+# Optional fields if submitting to a AAS journal too, see this blog post:
+# https://blog.joss.theoj.org/2018/12/a-new-collaboration-with-aas-publishing
+#aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it.
+#aas-journal: Astrophysical Journal <- The name of the AAS journal.
+---
+
+# Summary
+
+`macop` is an optimization Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
+
+# Motivation
+
+
+# Application
+
+Documentation with examples is available at [https://jbuisine.github.io/macop/](https://jbuisine.github.io/macop/).
+
+# Acknowledgements
+
+This work is supported by *Agence Nationale de la Recherche* : project ANR-17-CE38-0009
+
+# References