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- Implement fixed-point solver for OT barycenters with generic cost functions
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(generalizes ` ot.lp.free_support_barycenter ` ), with example. (PR #715 )
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- Implement fixed-point solver for barycenters between GMMs (PR #715 ), with example.
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+ - Fix documentation in the module ` ot.gaussian ` (PR #718 )
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#### Closed issues
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- Fixed ` ot.mapping ` solvers which depended on deprecated ` cvxpy ` ` ECOS ` solver (PR #692 , Issue #668 )
Original file line number Diff line number Diff line change @@ -354,7 +354,7 @@ def bures_wasserstein_barycenter(
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The function estimates the optimal barycenter of the
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empirical distributions. This is equivalent to resolving the fixed point
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- algorithm for multiple Gaussian distributions :math:`\left{\mathcal{N}(\mu,\Sigma)\right}_{i=1}^n`
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+ algorithm for multiple Gaussian distributions :math:`\left\ {\mathcal{N}(\mu,\Sigma)\right\ }_{i=1}^n`
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:ref:`[1] <references-OT-mapping-linear-barycenter>`.
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The barycenter still following a Gaussian distribution :math:`\mathcal{N}(\mu_b,\Sigma_b)`
@@ -452,7 +452,7 @@ def empirical_bures_wasserstein_barycenter(
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The function estimates the optimal barycenter of the
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empirical distributions. This is equivalent to resolving the fixed point
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- algorithm for multiple Gaussian distributions :math:`\left{\mathcal{N}(\mu,\Sigma)\right}_{i=1}^n`
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+ algorithm for multiple Gaussian distributions :math:`\left\ {\mathcal{N}(\mu,\Sigma)\right\ }_{i=1}^n`
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:ref:`[1] <references-OT-mapping-linear-barycenter>`.
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The barycenter still following a Gaussian distribution :math:`\mathcal{N}(\mu_b,\Sigma_b)`
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