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| author | Nathan Cassereau <84033440+ncassereau-idris@users.noreply.github.com> | 2022-06-13 14:49:55 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-06-13 14:49:55 +0200 |
| commit | e547fe30c59be72ae93c9f017786477b2652776f (patch) | |
| tree | a4e7802f02e0660d4da0821fb402bffc1a666858 /README.md | |
| parent | 1f307594244dd4c274b64d028823cbcfff302f37 (diff) | |
[MRG] Correct pointer overflow in EMD (#381)
* avoid overflow on openmp version of emd solver
* monothread version updated
* Fixed typo in readme
* added PR in releases
* typo in releases.md
* added a precision to releases.md
* added a precision to releases.md
* correct readme
* forgot to cast
* lower error
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -26,8 +26,8 @@ POT provides the following generic OT solvers (links to examples): * Debiased Sinkhorn barycenters [Sinkhorn divergence barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_debiased_barycenter.html) [37] * [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17]. * Weak OT solver between empirical distributions [39] -* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale). -* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12]), differentiable using gradients from +* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) with LP solver (only small scale). +* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12]), differentiable using gradients from Graph Dictionary Learning [38] * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24] * [Stochastic solver](https://pythonot.github.io/auto_examples/others/plot_stochastic.html) and |