I am writing an optimization code which is essentially this:

G = constant vector of size (40,1)

K = constant vector of size (37,1)

A = constant vector of size (50,1)

Meas = constant vector of size (50,1) (corresponds to the experimentally measured data)

I need to optimize a variable x of size (50,1) such that:

```
cvx_begin
cvx_solver mosek
variable x(50,1)
G = Exp_conv(x,A,G, K);
minimize(norm(G-Meas))
cvx_end
```

where Exp_conv is a function defined as:

```
function X = Exp_conv (x,A,G,K)
for i = 1:50
E = A(i).*((exp(x(i).*K))); %returns a (37,1) sized vector
n = 40-size(K,1);
E_final = [zeros(n,1);E]; %zero pad at one end to form a vector of the size of G
X(i,1) = sum(G.*E_final); %the output is (50,1) sized vector, corresponds to the observed data
end
end
```

The error is that norm cannot be calculated (cause the input to norm should be affine). Essentially, the objective norm(G-Meas) corresponds to a data fidelity term, where I try to minimize the error between the function constructed with the variable and the observed data. I have tried to think of any alternative formulation for this but cannot do it. Would be glad if anyone can suggest anything (hoping there is one!)