How to solve the problem using CVX.

We have a few observations of a matrix. Lets denote it by “D”. Since the observations are not accurate and some measurement error is involved, I want to solve this optimization problem: -

min_Z ||Z||_N such that ||E.*Z-D||_F<= epsilon.

Here Z is a matrix variable of dimension same as D.

E.*Z is the hadamard product of Z with E. And E represents the measurement matrix.

|| ||_N represents nuclear norm.

|| ||_F represents frobenious norm.

Now I typed something like this in my matlab code: -

```
cvx_begin sdp
variable Z(m,n) semidefinite;
norm(G.*Z(m,n)-D,'fro') <= epsilon ;
minimize(norm_nuc(Z(m,n)))
cvx_end
```

Its not working. I think I made some mistake. I will be really grateful if someone show me the right way to implement the optimization using CVX.

Thank you!

Shantanu.