Instead of initialising the probabilities for random sampling in SPDHG to be uniform, we could also add in optimal sampling example 5 in Matthias SIAM optimisation paper
I think in the paper they show optimal sampling when they know their functions are strongly convex. We don't yet have strong convexity options in SPDHG, although maybe we should!
https://github.com/TomographicImaging/CIL/blob/a2e45fe0aafe6329ec9027bda1cf262d4aff0284/Wrappers/Python/cil/optimisation/algorithms/SPDHG.py#L50C44-L50C44
Instead of initialising the probabilities for random sampling in SPDHG to be uniform, we could also add in optimal sampling example 5 in Matthias SIAM optimisation paper