I have the following toy example of an SDP:
cvx_begin sdp
variable M(2,2) symmetric
variable s
subject to
s*eye(2) >= M;
4*M >= s*eye(2);
M >= 0.1*eye(2);
minimize norm(M - [1 2; 3 4]);
cvx_end
I wonder if there is a way to formulate the two first LMI in CVX without the need to use the real slack variable s.