Slack variable in SDP


#1

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.