message: improving doc output
diff: | :figclass: align-center | Given a set of persistence diagrams :math:`\mu_1 \dots \mu_n`, it is | :Introduced in: GUDHI 3.1.0 |
  | | defined as a minimizer of the variance functional, that is of | |
  | Illustration of Frechet mean between persistence | :math:`\mu \mapsto \sum_{i=1}^n d_2(\mu,\mu_i)^2`. | :Copyright: MIT |
  - | diagrams. | where :math:`d_2` denotes the Wasserstein-2 distance between persis- | |
  - | | tence diagrams. | |
  + | diagrams. | where :math:`d_2` denotes the Wasserstein-2 distance between | |
  + | | persistence diagrams. | |
  | | It is known to exist and is generically unique. However, an exact | |
  - | | computation is in general untractable. Current implementation avai- | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 |
  - | | -lable is based on [Turner et al, 2014], and uses an EM-scheme to | |
  + | | computation is in general untractable. Current implementation | :Requires: Python Optimal Transport (POT) :math:`\geq` 0.5.1 |
  + | | available is based on [Turner et al, 2014], and uses an EM-scheme to | |
  | | provide a local minimum of the variance functional (somewhat similar | |
  | | to the Lloyd algorithm to estimate a solution to the k-means | |
  | | problem). The local minimum returned depends on the initialization of| |
  