From 0c0030f101e5d4b72f4b48f45cafdf9fe71b2aa5 Mon Sep 17 00:00:00 2001 From: akprasadan Date: Sat, 20 Jun 2026 14:41:24 -0700 Subject: [PATCH 1/2] Fix documentation for lowrank_sinkhorn and lowrank_gromov_wasserstein_samples. --- ot/gromov/_lowrank.py | 10 +++++----- ot/lowrank.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/ot/gromov/_lowrank.py b/ot/gromov/_lowrank.py index b7f5c49ed..ca7e1f72f 100644 --- a/ot/gromov/_lowrank.py +++ b/ot/gromov/_lowrank.py @@ -93,7 +93,7 @@ def lowrank_gromov_wasserstein_samples( where : - :math:`A` is the (`dim_a`, `dim_a`) square pairwise cost matrix of the source domain. - - :math:`B` is the (`dim_a`, `dim_a`) square pairwise cost matrix of the target domain. + - :math:`B` is the (`dim_b`, `dim_b`) square pairwise cost matrix of the target domain. - :math:`\mathcal{Q}_{A,B}` is quadratic objective function of the Gromov Wasserstein plan. - :math:`Q` and `R` are the low-rank matrix decomposition of the Gromov-Wasserstein plan. - :math:`g` is the weight vector for the low-rank decomposition of the Gromov-Wasserstein plan. @@ -119,9 +119,9 @@ def lowrank_gromov_wasserstein_samples( Regularization term >=0 rank : int, optional. Default is None. (>0) Nonnegative rank of the OT plan. If None, min(ns, nt) is considered. - alpha : int, optional. Default is 1e-10. (>0 and <1/r) + alpha : float, optional. Default is 1e-10. (>0 and <1/r) Lower bound for the weight vector g. - rescale_cost : bool, optional. Default is False + rescale_cost : bool, optional. Default is True Rescale the low rank factorization of the sqeuclidean cost matrix seed_init : int, optional. Default is 49. (>0) Random state for the 'random' initialization of low rank couplings @@ -135,9 +135,9 @@ def lowrank_gromov_wasserstein_samples( Stop threshold on error (>0) for Low Rank GW The error is the sum of Kullback Divergences computed for each low rank coupling (Q, R and g) and scaled using gamma. - numItermax_dykstra : int, optional. Default is 2000. + numItermax_dykstra : int, optional. Default is 10000. Max number of iterations for the Dykstra algorithm - stopThr_dykstra : float, optional. Default is 1e-7. + stopThr_dykstra : float, optional. Default is 1e-3. Stop threshold on error (>0) in Dykstra cost_factorized_Xs: tuple, optional. Default is None Tuple with two pre-computed low rank decompositions (A1, A2) of the source cost diff --git a/ot/lowrank.py b/ot/lowrank.py index f4e3aa413..923e7f29b 100644 --- a/ot/lowrank.py +++ b/ot/lowrank.py @@ -374,7 +374,7 @@ def lowrank_sinkhorn( Nonnegative rank of the OT plan. If None, min(ns, nt) is considered. alpha : int, optional. Default is 1e-10. (>0 and <1/r) Lower bound for the weight vector g. - rescale_cost : bool, optional. Default is False + rescale_cost : bool, optional. Default is True Rescale the low rank factorization of the sqeuclidean cost matrix init : str, optional. Default is 'random'. Initialization strategy for the low rank couplings. 'random', 'deterministic' or 'kmeans' From 08e21ba25ddcf434acbd5e69fb41ab8a830f8974 Mon Sep 17 00:00:00 2001 From: akprasadan <40134413+akprasadan@users.noreply.github.com> Date: Mon, 22 Jun 2026 23:29:20 -0700 Subject: [PATCH 2/2] Update RELEASES.md Add docstring fix to RELEASES.MD --- RELEASES.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/RELEASES.md b/RELEASES.md index f3c17d427..4b744eb15 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -32,7 +32,8 @@ This new release adds support for sparse cost matrices and a new lazy EMD solver implemented in `ot.sgot` (PR #792) - Add batch FUGW loss to `ot.batch` and fix issues in some default parameters in the batch module (PR #775) - Build wheels on ubuntu ARM to avoid QEMU emulation (PR #818) -- Add new methods to compute the linear transport map and the related 2-Wasserstein distance betweeen high-dimensional (HD) Gaussian distributions as described in [88], implemented in `ot.gaussian.bures_wasserstein_mapping_hd` and `ot.gaussian.bures_wasserstein_distance_hd`, respectively. Two additional methods estimate the same quantities from the source and destination observed data and are implemented in `ot.gaussian.empirical_bures_wasserstein_mapping_hd` and `ot.gaussian.empirical_bures_wasserstein_distance_hd`, respectively (PR #814) +- Add new methods to compute the linear transport map and the related 2-Wasserstein distance betweeen high-dimensional (HD) Gaussian distributions as described in [88], implemented in `ot.gaussian.bures_wasserstein_mapping_hd` and `ot.gaussian.bures_wasserstein_distance_hd`, respectively. Two additional methods estimate the same quantities from the source and destination observed data and are implemented in `ot.gaussian.empirical_bures_wasserstein_mapping_hd` and `ot.gaussian.empirical_bures_wasserstein_distance_hd`, respectively (PR #814) +- Fix docstrings for `lowrank_gromov_wasserstein_samples` and `lowrank_sinkhorn` (PR #823) #### Closed issues