Introduce unsquare slack matrix variables in SDP Programming Mode constraint

Hi all,
My problem is in SDP programming mode. I have one constraint which is min(…) > threshold. So I formulate the problem and introduce slack variable as follows
cvx_begin sdp
variable X(M, N) nonnegative; <-slack variables
variable Q(:, :, : ) hermitian semidefinite;
variable eta nonnegative;

    maximize( eta )
    subject to

    for m=1:M 
           tmp = cvx(zeros(1,1));
           for n=1:N
                 ... >=  X(m, n);
                 X(m, n)  >= threshold;
           tmp = tmp + X(m, n);

However, CVX does not accept with an error “SDP constraint must be square.” Exactly the error comes from X(M,N) due to wrong declare. So how can I reformulate the slack variable X(M,N) to obtain the constraint min(…) >= threhold?
Thanks so much

I don’t understand what you’re doing. What is ... in ... >= X(m, n); ?

Anyhow, does the following address your problem?

When SDP mode is engaged, CVX interprets certain inequality constraints in a different manner. To be specific:

Inequality constraints involving non-square matrices are disallowed; attempting to use them causes an error. If you wish to do true elementwise comparison of matrices X and Y, use a vectorization operation X(:) <= Y(:) or vec( X ) <= vec( Y ). (vec is a function provided by CVX that is equivalent to the colon operation.)