Open HIROJAPAN opened 3 years ago
If I understand your question, are you trying to compute the "L_p,q norm" of the signal?
Image a = conv(A, x)
Image b = conv(B, x)
Problem = group_norm(vstack(reshape_to_row_vector(a), reshape_to_row_vector(b)), p = 'Infinity', q = 2).
Yes you can express this problem with ProxImaL, but you will have to implement your own proximal function for L_p,q
. Also, deferring to @SteveDiamond regarding the convexity of such problem.
For general convex optimization problems, I also suggest you post the questions on https://github.com/cvxpy/cvxpy . They have a larger community.
This could also be implemented as an elementwise maximum of a
and b
. This is convex and would be pretty easy to add.
Is it possible to apply two convolution filters to the variable X, compare the elements of the two arrays, and choose the larger value? And can ProximaL solve the problem?