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diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 13cf572..625cebf 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -11,7 +11,8 @@ It provides the following solvers: - OT solver for the linear program/ Earth Movers Distance [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10]. + and stabilized version [9][10] with optional GPU implementation + (required cudamat). - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -71,14 +72,14 @@ Dependencies Some sub-modules require additional dependences which are discussed below -- ot.dr (Wasserstein dimensionality rediuction) depends on autograd and - pymanopt that can be installed with: +- **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd + and pymanopt that can be installed with: :: pip install pymanopt autograd -- ot.gpu (GPU accelerated OT) depends on cudamat that have to be +- **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be installed with: :: @@ -87,6 +88,8 @@ below cd cudamat python setup.py install --user # for user install (no root) +obviously you need CUDA installed and a compatible GPU. + Examples -------- @@ -144,47 +147,59 @@ References ---------- [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, -December). Displacement interpolation using Lagrangian mass transport. +December). `Displacement interpolation using Lagrangian mass +transport <https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf>`__. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of -optimal transport. In Advances in Neural Information Processing Systems -(pp. 2292-2300). +[2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of +optimal transport <https://arxiv.org/pdf/1306.0895.pdf>`__. In Advances +in Neural Information Processing Systems (pp. 2292-2300). [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. -(2015). Iterative Bregman projections for regularized transportation -problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. +(2015). `Iterative Bregman projections for regularized transportation +problems <https://arxiv.org/pdf/1412.5154.pdf>`__. SIAM Journal on +Scientific Computing, 37(2), A1111-A1138. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, -Supervised planetary unmixing with optimal transport, Whorkshop on -Hyperspectral Image and Signal Processing : Evolution in Remote Sensing -(WHISPERS), 2016. +`Supervised planetary unmixing with optimal +transport <https://hal.archives-ouvertes.fr/hal-01377236/document>`__, +Whorkshop on Hyperspectral Image and Signal Processing : Evolution in +Remote Sensing (WHISPERS), 2016. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport -for Domain Adaptation," in IEEE Transactions on Pattern Analysis and -Machine Intelligence , vol.PP, no.99, pp.1-1 +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, `Optimal Transport +for Domain Adaptation <https://arxiv.org/pdf/1507.00504.pdf>`__, in IEEE +Transactions on Pattern Analysis and Machine Intelligence , vol.PP, +no.99, pp.1-1 [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging -Sciences, 7(3), 1853-1882. +`Regularized discrete optimal +transport <https://arxiv.org/pdf/1307.5551.pdf>`__. SIAM Journal on +Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. arXiv -preprint arXiv:1510.06567. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). `Generalized +conditional gradient: analysis of convergence and +applications <https://arxiv.org/pdf/1510.06567.pdf>`__. arXiv preprint +arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation -for discrete optimal transport", Neural Information Processing Systems -(NIPS), 2016. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, `Mapping estimation +for discrete optimal +transport <http://remi.flamary.com/biblio/perrot2016mapping.pdf>`__, +Neural Information Processing Systems (NIPS), 2016. -[9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for -Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. +[9] Schmitzer, B. (2016). `Stabilized Sparse Scaling Algorithms for +Entropy Regularized Transport +Problems <https://arxiv.org/pdf/1610.06519.pdf>`__. arXiv preprint +arXiv:1610.06519. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). -Scaling algorithms for unbalanced transport problems. arXiv preprint +`Scaling algorithms for unbalanced transport +problems <https://arxiv.org/pdf/1607.05816.pdf>`__. arXiv preprint arXiv:1607.05816. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). -Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. +`Wasserstein Discriminant +Analysis <https://arxiv.org/pdf/1608.08063.pdf>`__. arXiv preprint +arXiv:1608.08063. .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT |