Added Huber loss function class

[msaca-okse]

Description

The Huber loss function behaves like the L2 loss close to 0, and like L1 loss for large values. The loss function is differentiable and can thus be used in GD or FISTA. It's an alternative to L2 loss that is more robust to extreme values. The implementation mirrors that of "cil.optimisation.functions.LeastSquares". The Huber loss has a parameter that selects when the cutoff between L2 and L1 loss happens.

Example Usage

G = IndicatorBox(lower=0.0)
huber_delta = 1.0
F = HuberLoss(A,b,huber_delta)
myFISTA = FISTA(f=F, 
                  g=G, 
                  initial=x0)

Contribution Notes

• The content of this Pull Request (the Contribution) is intentionally submitted for inclusion in CIL (the Work) under the terms and conditions of the Apache-2.0 License
• I confirm that the contribution does not violate any intellectual property rights of third parties

[msaca-okse]

Fixes #2281