```
Y=AS + N
size of S: K × T
size of A: M × K
min norm(p,1)
subject to
[frobenius norm of(Y-AS)]^2≤β^2
p(i)=norm(one row of the S,2)
P=[P(i),....,p(K)];
```

How can code this problem in CVX(with the help of e.g. SeDuMi) to find S?

```
Y=AS + N
size of S: K × T
size of A: M × K
min norm(p,1)
subject to
[frobenius norm of(Y-AS)]^2≤β^2
p(i)=norm(one row of the S,2)
P=[P(i),....,p(K)];
```

How can code this problem in CVX(with the help of e.g. SeDuMi) to find S?

This looks like a good old fashioned sum-of-norms problem. Then only trick here is that you want the norms of the *rows* of S, and CVX’s `norms`

function nominally looks at the norms of the columns. An easy way to fix that is to just transpose S.

```
cvx_begin
variable S(K,T)
minimize(sum(norms(S')))
subject to
norm(Y-A*S,'fro')<=beta
cvx_end
```

Or alternatively use sum(norms(S,2,2)) ?