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author | Rémi Flamary <remi.flamary@gmail.com> | 2018-05-31 13:03:40 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-05-31 13:03:40 +0200 |
commit | ed0d4171c6291a15360bdb8a955b0783585da749 (patch) | |
tree | bc9e63e628ce3138d3ad8547115559ab7b1496a2 | |
parent | 10f9b0d1b02c2b5f4c4eeac0c1f803657c89764b (diff) |
update readme
-rw-r--r-- | README.md | 3 | ||||
-rw-r--r-- | examples/plot_OT_1D_smooth.py | 6 | ||||
-rw-r--r-- | ot/smooth.py | 9 |
3 files changed, 15 insertions, 3 deletions
@@ -15,6 +15,7 @@ It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). +* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularization [17]. * Non regularized Wasserstein barycenters [16] with LP solver. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] @@ -213,3 +214,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . [16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py index 4927617..dec84db 100644 --- a/examples/plot_OT_1D_smooth.py +++ b/examples/plot_OT_1D_smooth.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -==================== -1D optimal transport -==================== +=========================== +1D smooth optimal transport +=========================== This example illustrates the computation of EMD, Sinkhorn and smooth OT plans and their visualization. diff --git a/ot/smooth.py b/ot/smooth.py index 1a9972b..b3649e9 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -22,6 +22,7 @@ #OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF #THE POSSIBILITY OF SUCH DAMAGE. + # Author: Mathieu Blondel # Remi Flamary <remi.flamary@unice.fr> @@ -31,6 +32,13 @@ Smooth and Sparse Optimal Transport. Mathieu Blondel, Vivien Seguy, Antoine Rolet. In Proc. of AISTATS 2018. https://arxiv.org/abs/1710.06276 + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal +Transport. Proceedings of the Twenty-First International Conference on +Artificial Intelligence and Statistics (AISTATS). + +Original code from https://github.com/mblondel/smooth-ot/ + """ import numpy as np @@ -402,6 +410,7 @@ def get_plan_from_semi_dual(alpha, b, C, regul): def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, numItermax=500, log=False): + if reg_type.lower() in ['l2', 'squaredl2']: regul = SquaredL2(gamma=reg) |