0.9.5

This new release contains several new features, starting with a novel Gaussian Mixture Model Optimal Transport (GMM-OT) solver to compare GMM while enforcing the transport plan to remain a GMM, that benefits from a closed-form solution making it practical for high-dimensional matching problems. We also extended our general unbalanced OT solvers to support any non-negative reference measure in the regularization terms, before adding the novel translation invariant UOT solver showcasing a higher convergence speed.
We also implemented several new solvers and enhanced existing ones to perform OT across spaces. These include a semi-relaxed FGW barycenter solver, coupled with new initialization heuristics for the inner divergence computation, to perform graph partitioning or dictionary learning. Followed by novel unbalanced FGW and Co-optimal transport solvers to promote robustness to outliers in such matching problems. And we finally updated the implementation of partial GW now supporting asymmetric structures and the KL divergence, while leveraging a new generic conditional gradient solver for partial transport problems enabling significant speed improvements. These latest updates required some modifications to the line search functions of our generic conditional gradient solver, paving the way for future improvements to other GW-based solvers.
Last but not least, we implemented a pre-commit scheme to automatically correct common programming mistakes likely to be made by our future contributors.

This release also contains few bug fixes, concerning the support of any metric in ot.emd_1d / ot.emd2_1d, and the support of any weights in ot.gaussian.

Breaking change

• Custom functions provided as parameter line_search to ot.optim.generic_conditional_gradient must now have the signature line_search(cost, G, deltaG, Mi, cost_G, df_G, **kwargs), adding as input df_G the gradient of the regularizer evaluated at the transport plan G. This change aims at improving speed of solvers having quadratic polynomial functions as regularizer such as the Gromov-Wassertein loss (PR #663).

New features

• New linter based on pre-commit using ruff, codespell and yamllint (PR #681)
• Added feature mass=True for nx.kl_div (PR #654)
• Implemented Gaussian Mixture Model OT ot.gmm (PR #649)
• Added feature semirelaxed_fgw_barycenters and generic FGW-related barycenter updates update_barycenter_structure and update_barycenter_feature (PR #659)
• Added initialization heuristics for sr(F)GW problems via semirelaxed_init_plan, integrated in all sr(F)GW solvers (PR #659)
• Improved ot.plot.plot1D_mat (PR #649)
• Added nx.det (PR #649)
• nx.sqrtm is now broadcastable (takes ..., d, d) inputs (PR #649)
• Restructured ot.unbalanced module (PR #658)
• Added ot.unbalanced.lbfgsb_unbalanced2 and add flexible reference measure c in all unbalanced solvers (PR #658)
• Implemented Fused unbalanced Gromov-Wasserstein and unbalanced Co-Optimal Transport (PR #677)
• Notes before depreciating partial Gromov-Wasserstein function in ot.partial moved to ot.gromov (PR #663)
• Create ot.gromov._partial add new features loss_fun = "kl_loss" and symmetry=False to all solvers while increasing speed + updating adequatly ot.solvers (PR #663)
• Added ot.unbalanced.sinkhorn_unbalanced_translation_invariant (PR #676)

Closed issues

• Fixed ot.gaussian ignoring weights when computing means (PR #649, Issue #648)
• Fixed ot.emd_1d and ot.emd2_1d incorrectly allowing any metric (PR #670, Issue #669)

Full Changelog: 0.9.4...0.9.5