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Commit 803d2ab

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Minor typos in the documentations (#756)
* Fixed last author's name * Fixed minor typos in the User Guide
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‎README.md‎

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@@ -383,7 +383,7 @@ Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Confer
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[50] Liu, T., Puigcerver, J., & Blondel, M. (2023). [Sparsity-constrained optimal transport](https://openreview.net/forum?id=yHY9NbQJ5BP). Proceedings of the Eleventh International Conference on Learning Representations (ICLR).
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[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
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[51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
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[52] Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023). [Entropic Wasserstein Component Analysis](https://arxiv.org/abs/2303.05119). ArXiv.
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‎docs/source/user_guide.rst‎

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In addition to all those variants of Sinkhorn, we have another
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implementation solving the problem in the smooth dual or semi-dual in
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:any:`ot.smooth`. This solver uses the :any:`scipy.optimize.minimize`
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function to solve the smooth problem with :code:`L-BFGS-B` algorithm. Tu use
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function to solve the smooth problem with :code:`L-BFGS-B` algorithm. To use
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this solver, use functions :any:`ot.smooth.smooth_ot_dual` or
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:any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to
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choose entropic/Kullbach-Leibler regularization.
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choose entropic/Kullback-Leibler regularization.
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**Choosing a Sinkhorn solver**
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‎examples/gromov/plot_fgw_solvers.py‎

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"Optimal Transport for structured data with application on graphs"
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International Conference on Machine Learning (ICML). 2019.
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[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019).
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[51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019).
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"Gromov-wasserstein learning for graph matching and node embedding".
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In International Conference on Machine Learning (ICML), 2019.
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‎examples/gromov/plot_gromov.py‎

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[33] Kerdoncuff T., Emonet R., Marc S. "Sampled Gromov Wasserstein",
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Machine Learning Journal (MJL), 2021.
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[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019).
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[51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019).
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"Gromov-wasserstein learning for graph matching and node embedding".
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In International Conference on Machine Learning (ICML), 2019.
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‎ot/gromov/_bregman.py‎

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distance between networks and stable network invariants.
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Information and Inference: A Journal of the IMA, 8(4), 757-787.
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.. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
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.. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
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learning for graph matching and node embedding. In International
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Conference on Machine Learning (ICML), 2019.
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"""
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"Gromov-Wasserstein averaging of kernel and distance matrices."
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International Conference on Machine Learning (ICML). 2016.
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.. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
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.. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
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learning for graph matching and node embedding. In International
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Conference on Machine Learning (ICML), 2019.
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"""
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distance between networks and stable network invariants.
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Information and Inference: A Journal of the IMA, 8(4), 757-787.
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.. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
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.. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
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learning for graph matching and node embedding. In International
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Conference on Machine Learning (ICML), 2019.
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@@ -1358,7 +1358,7 @@ def entropic_fused_gromov_wasserstein2(
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"Gromov-Wasserstein averaging of kernel and distance matrices."
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International Conference on Machine Learning (ICML). 2016.
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.. [51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). Gromov-wasserstein
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.. [51] Xu, H., Luo, D., Zha, H., & Carin, L. (2019). Gromov-wasserstein
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learning for graph matching and node embedding. In International
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Conference on Machine Learning (ICML), 2019.
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