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3 changes: 3 additions & 0 deletions RELEASES.md
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#### New features


- `ot.dr.wda` gains `solver='torch'`, a PyTorch autodiff solver with Riemannian gradient descent, usable on installations without autograd or pymanopt, and accepting torch tensors directly (PR #858, Issue #806)
- `ot.dr` dependencies (autograd, pymanopt, scikit-learn, torch) are now imported optionally, so importing `ot.dr` no longer requires all of them; each function raises an explicit `ImportError` naming what it needs (PR #858)
- Use `ot.utils.check_marginal` (and shape-tuple support in `ot.utils.unif`) to fill and validate default marginals consistently across solvers (Gromov, low-rank, stochastic, barycenter, factored) (PR #856)
- Add stereographic spherical sliced Wasserstein distance in `ot.sliced.stereographic_sliced_wasserstein_sphere`, with its rotationally invariant extension (PR #836)
- Add Quasi-Monte Carlo sliced Wasserstein sampling (QSW/RQSW) via generalized
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