I am going to solve an linear iterative inverse problem. I have two functions in matlab
which one of them play the forward
and the other play the adjoint
role. I am not familiar with inverse problems very much and I am going to test some algorithms to check which one can help me to obtain a sparse model. Since I have to use my functions to do the forward and inverse operator roles in Ax=b
, so I think I am not able to use some algorithms which needs A
as a matrix. As an example, I could use the linearized bregman
as follows:
$$ v^{k+1} = v^k + A^T(f-Au^k). $$
$$ u^{k+1}= \delta * shrink(v^{k+1},1/\mu). $$
in which, for A
I use that function which transform the model u
to the data space and for A^T
the function which transform the data to the model space.
I want to know that is there any other iterative algorithm which can be used in this manner for sparsity?