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+References
+==========
+
+Bibliography
+------------
+.. bibliography::
+    :cited:
+
+Glossary
+--------
+.. glossary::
+
+    OT
+        An `optimal transport <https://en.wikipedia.org/wiki/Transportation_theory_(mathematics)>`_ problem is defined
+        as a matching task between distributions, e.g. sets of cells.
+
+    transport matrix
+        The output of a discrete :term:`OT` problem indicating how much mass from data point :math:`x_i` in row
+        :math:`i` is transported to data point :math:`y_j` in column :math:`j`.
+
+    entropic regularization
+        Entropy regularization of :term:`OT` problems :cite:`cuturi:2013` reduces the time complexity and allows for
+        more desirable statistical properties. The higher the entropy regularization, the more diffused the OT solution.
+
+    marginals
+        An :term:`OT` problem matches distributions, e.g. set of cells. The distribution is defined by the location
+        of a cell, e.g. in gene expression space, and the weight assigned to one cell.
+
+
+    balanced OT problem
+        :term:`OT` problem where the :term:`marginals` are fixed. Each data point (cell) of the source distribution
+        emits a certain amount of mass given by the source :term:`marginals`, and each data point (cell) of the target
+        distribution receives a certain amount of mass given by the target :term:`marginals`.
+
+    unbalanced OT problem
+        :term:`OT` problem where the :term:`marginals` are not fixed. If beneficial, a data point might emit or
+        receive more or less mass than prescribed by the :term:`marginals`. The larger the unbalancedness parameters
+        ``tau_a`` and ``tau_b``, the more the mass emitted, and received, respectively, can deviate from the
+        :term:`marginals` :cite:`chizat:18`.
+
+    linear problem
+        :term:`OT` problem only containing a :term:`linear term` and no :term:`quadratic term`.
+
+    linear term
+        Term of the cost function on the shared space, e.g. gene expression space.
+
+    quadratic problem
+        :term:`OT` problem containing a :term:`quadratic term` and possibly a :term:`linear term`.
+
+    quadratic term
+        Term of the cost function comparing two different spaces.
+
+    Gromov-Wasserstein
+        :term:`OT` problem between two distributions where a data point, e.g. a cell. in the source distribution
+        does not live in the same space as a data point in the target distribution. Such problem is a
+        :term:`quadratic problem`.
+
+    fused Gromov-Wasserstein
+        :term:`OT` problem between two distributions where a data point, e.g. a cell, of the source distribution
+        has both features in the same space as the target distribution (:term:`linear term`) and features in a
+        different space than a data point in the target distribution (:term:`quadratic term`). Such problem is a
+        :term:`quadratic problem`.
+
+    dual potentials
+        Potentials obtained by the :term:`Sinkhorn` algorithm which define the solution of a :term:`linear problem`
+        :cite:`cuturi:2013`. These weights are referred to as `marginals`.
+
+    Sinkhorn
+        The Sinkhorn algorithm :cite:`cuturi:2013` is used for solving a :term:`linear problem`, and is also used
+        in inner iterations for solving a :term:`quadratic problem`.
+
+    low-rank OT
+        `low-rank <https://en.wikipedia.org/wiki/Low-rank_approximation>`_ OT approximates full-rank :term:`OT`,
+        which allows for faster computations and lower memory complexity
+        :cite:`scetbon:21a,scetbon:21b,scetbon:22b,scetbon:23`. The :term:`transport matrix`
+        will be :term:`low-rank`.
+
+    low-rank
+        If the OT problem is solved with a `low-rank <https://en.wikipedia.org/wiki/Low-rank_approximation>`_ solver,
+        the :term:`transport matrix` is the product of several low-rank matrices (i.e. lower than the number of data
+        points in the source distribution and the target distribution).