How to improve the efficiency of CVX code

I have a minimize problem and the cvx code is followed as:

  variable x(n);
  for i = 1:c
       ee(i) = norm(T(:,:,i)*x-y,2)/norm(y,2);
   minimize (norm(D*x-y,2)+0.01*sum(ee));

In the objective function, only x is variable and others are all known. n is the dimension of vector x and c is just a loop number less than 100. I am sorry I do not use other solvers to this problem but the cost time is unacceptable. Do you think there is no improvement space?

In my problem, the size of n is 700 and the size of y is a 300*1 vector. I have no idea why for this problem, CVX should cost so much time. How can i know the detailed optimization method which cvx used for my problem?

This code will cost 17 minutes to compute the result. It’s so slow and i don’t know how to speed up. Thank you very much!

What solver are you using? How do the other solvers compare? Also, since we don’t know what “n” is, what “c” is, and what the size of y is, it’s hard to say if 17 minutes is reasonable or not. I see no reason for CVX to be unusually slow on this problem.

Thanks for the edit but I still do not know what the size of “n” is, and what the size of y is. If the solver time is unacceptable, then I’m afraid you will have to use another system besides CVX.

Please provide a complete script to reproduce the problem. It ought to be mandatory and common sense. If you provided such information, we could experiment with different solvers in a couple minutes and make suggestions for alternative formulations.