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Types of changes
This releases introduces several new notable features. The less important but most exiting one being that we now have a logo for the toolbox (color and dark background) :
This logo is generated using with matplotlib and using the solution of an OT problem provided by POT (with
ot.emd
). Generating the logo can be done with a simple python script also provided in the documentation gallery.New OT solvers include Weak OT and OT with factored coupling that can be used on large datasets. The Majorization Minimization solvers for non-regularized Unbalanced OT are now also available. We also now provide an implementation of GW and FGW unmixing and dictionary learning. It is now possible to use autodiff to solve entropic an quadratic regularized OT in the dual for full or stochastic optimization thanks to the new functions to compute the dual loss for entropic and quadratic regularized OT and reconstruct the OT plan on part or all of the data. They can be used for instance to solve OT problems with stochastic gradient or for estimating the dual potentials as neural networks.
On the backend front, we now have backend compatible functions and classes in the domain adaptation
ot.da
and unbalanced OTot.unbalanced
modules. This means that the DA classes can be used on tensors from all compatible backends. The free support Wasserstein barycenter solver is now also backend compatible.Finally we have worked on the documentation to provide an update of existing examples in the gallery and and several new examples including GW dictionary learning and weak Optimal Transport.
PR checklist