Backpropagation proximal gradient

[mblum94]

Dear @khammernik ,

I've got a question/comment concerning back propagation of the proximal gradient layer with respect to lambda.
I got curious, reading your current MRM paper where you wrote that training becomes unstable when lambda is not fixed.

Following your conventions,
M := lambda A^HA + 1
Q := M^-1.

Now, the the derivative of the inverse of a matrix (with respect to lambda) is given by:
Q'=-Q M' Q

In the code, I see twice the Q as expected but not M' = A^HA.
Is it missing or does it cancel somehow?

Best regards,
Moritz