refactoring UnivariateSolver

[marcocuturi]

Follow up on @Daniel-Packer 's implementation

• New API for UnivariateSolver more in agreement with other implementations (takes a problem as input now)
  • In particular, UnivariateSolver can now take a standard prob on point clouds of size [n,d]. This means that d independent slices will be considered, running 1D OT on each of them.
• Implementation for fractional costs a and b of all flavors, and simplifications (essentially no need to pass method string, only arguments such as num_subsamples or quantiles. If those are passed then user intends to use these alternative methods. Otherwise, select automatically the good method by checking problem's nature (in particular n=m and weights were uniform (only weaknesses: only checks if those were by default, don't see obvious workaround to test numerically)
• No option to use soft sorting operators. Not completely sure this makes so much sense as of now, so I simplified
• Also Output OT matrix, even for fractional cost, in a new UnivariateOutput namedtuple.
• UnivariateWasserstein is now a new cost for point clouds. This makes sense for lower_bound.py and slightly alleviates code there, rather than using the double vmap that is considered by default in the .pairwise method of a CostFn

[codecov]

Codecov Report

Merging #472 (6a77a9b) into main (75cdd11) will decrease coverage by 0.04%.
The diff coverage is 86.66%.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #472      +/-   ##
==========================================
- Coverage   90.45%   90.41%   -0.04%     
==========================================
  Files          58       59       +1     
  Lines        6350     6440      +90     
  Branches      894      912      +18     
==========================================
+ Hits         5744     5823      +79     
- Misses        467      472       +5     
- Partials      139      145       +6     

Files	Coverage Δ
src/ott/geometry/distrib_costs.py	100.00% <100.00%> (ø)
src/ott/math/utils.py	95.00% <100.00%> (+0.45%)	⬆️
src/ott/problems/linear/linear_problem.py	100.00% <100.00%> (ø)
src/ott/tools/sinkhorn_divergence.py	91.04% <100.00%> (ø)
src/ott/solvers/quadratic/lower_bound.py	75.00% <46.15%> (-5.77%)	⬇️
src/ott/solvers/linear/univariate.py	88.07% <87.00%> (-6.48%)	⬇️

... and 1 file with indirect coverage changes

[marcocuturi]

also of interest for @JTT94 since could impact soft sorting initializers