How to express this normalized square objective function?

Let \bf d is a vector of size 1\times N
\bf s is also a vector of size 1\times N

Now, the objective I have is

\text{minimize }\max\hspace{1mm} \left(\frac{d_i-s_i}{d_i}\right)^2

Is there a way to express it in CVX?

Have you proven it is convex, as is your responsibility? Why isn't CVX accepting my model? READ THIS FIRST!

According to my calculations, the Hessian of ((d - s)/s)^2 evaluated at d = 2, s = 1 has one positive and one negative eigenvalue, so not convex. Actually, I think it is indefinite (neither convex nor concave) everywhere except for d = s.