The \gamma_2 norm of a real m\times n matrix A is defined as $$\gamma_2(A)=max_{u,v}|A\circ uv^T|_{tr},subject,to,|u|_2=|v|_2=1$$ where \|\|_{tr} is the trace norm of a matrix and \circ is the Hadamard product of two matrices. Lee et al [1] give a SDP formulation of this norm, which is as follows: $$\gamma_2(A)=min ,t$$ $$subject,to,\left( \begin{array}{ccc}
W_1 & A\
A^T & W_2 \\end{array} \right)$$ $$diag(W_1)\leq t$$ $$diag(W_2)\leq t$$
So, I need some help to express this model in a form from which the CVX modelling could be obvious. As my SDP modelling skills became a bit rusty, I find it difficult to do it by myself.

[1] Lee. J, et al, Practical Large-Scale Optimization for Max-Norm Regularization, NIPS 2010

Thanks for helping me! By reading the DCP ruleset, I concluded that CVX can’t handle the first constraint of my problem, so I tried to convert it to the standard SDP form and this was what troubled me.