Add regularized costs

.github/workflows/tests.yml

@@ -45,10 +45,12 @@ jobs:
     steps:
     - uses: actions/checkout@v3
     - name: Install dependencies
-      # `jax[cuda]<0.4` because of: https://github.com/google/jax/issues/13758
+      # `jax[cuda]<0.4` because of Docker issues: https://github.com/google/jax/issues/13758
+      # `flax<0.6.5` because it requires `jax>=0.4.2`
       run: |
         python3 -m pip install --upgrade pip
         python3 -m pip install -e".[test]"
+        python3 -m pip install "flax<0.6.5"
         python3 -m pip install "jax[cuda]<0.4" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
 
     - name: Nvidia SMI

docs/geometry.rst

@@ -52,15 +52,16 @@ Cost Functions
 .. autosummary::
     :toctree: _autosummary
 
-    costs.CostFn
-    costs.TICost
     costs.SqPNorm
     costs.PNormP
     costs.SqEuclidean
     costs.Euclidean
     costs.Cosine
     costs.Bures
     costs.UnbalancedBures
+    costs.ElasticL1
+    costs.ElasticSTVS
+    costs.ElasticSqKOverlap
 
 Utilities
 ---------

docs/index.rst

@@ -15,13 +15,13 @@ as differentiable approximations to ranking or even clustering.
 
 To achieve this, ``OTT`` rests on two families of tools:
 
-  - the first family consists in *discrete* solvers computing transport between
-    point clouds, using the Sinkhorn :cite:`cuturi:13` and low-rank Sinkhorn
-    :cite:`scetbon:21` algorithms, and moving up towards Gromov-Wasserstein
-    :cite:`memoli:11,peyre:16`;
-  - the second family consists in *continuous* solvers, using suitable neural
-    architectures :cite:`amos:17` coupled with SGD type estimators
-    :cite:`makkuva:20,korotin:21`.
+- the first family consists in *discrete* solvers computing transport between
+  point clouds, using the Sinkhorn :cite:`cuturi:13` and low-rank Sinkhorn
+  :cite:`scetbon:21` algorithms, and moving up towards Gromov-Wasserstein
+  :cite:`memoli:11,peyre:16`;
+- the second family consists in *continuous* solvers, using suitable neural
+  architectures :cite:`amos:17` coupled with SGD type estimators
+  :cite:`makkuva:20,korotin:21`.
 
 Installation
 ------------

docs/references.bib

@@ -703,3 +703,40 @@ @ARTICLE{chen:20
   pages={2133-2147},
   doi={10.1109/TPAMI.2019.2908635}
 }
+
+@ARTICLE{schreck:15,
+  author={Schreck, Amandine and Fort, Gersende and Le Corff, Sylvain and Moulines, Eric},
+  journal={IEEE Journal of Selected Topics in Signal Processing},
+  title={A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection},
+  year={2016},
+  volume={10},
+  number={2},
+  pages={366-375},
+  doi={10.1109/JSTSP.2015.2496546}
+}
+
+@inproceedings{argyriou:12,
+  author = {Argyriou, Andreas and Foygel, Rina and Srebro, Nathan},
+  booktitle = {Advances in Neural Information Processing Systems},
+  editor = {F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger},
+  pages = {},
+  publisher = {Curran Associates, Inc.},
+  title = {Sparse Prediction with the k-Support Norm},
+  url = {https://proceedings.neurips.cc/paper/2012/file/99bcfcd754a98ce89cb86f73acc04645-Paper.pdf},
+  volume = {25},
+  year = {2012}
+}
+
+@article{zou:05,
+  author = {Zou, Hui and Hastie, Trevor},
+  title = {Regularization and variable selection via the elastic net},
+  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
+  volume = {67},
+  number = {2},
+  pages = {301-320},
+  keywords = {Grouping effect, LARS algorithm, Lasso, Penalization, p≫n problem, Variable selection},
+  doi = {https://doi.org/10.1111/j.1467-9868.2005.00503.x},
+  url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9868.2005.00503.x},
+  eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9868.2005.00503.x},
+  year = {2005}
+}

pyproject.toml

@@ -49,6 +49,7 @@ classifiers = [
     "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
 ]
 
 [project.urls]