dd 10% noise.
R = 5;
info = create_problem('Size', [50 40 30], 'Num_Factors', R, 'Noise', 0.10);
X = info.Data;
M_true = info.Soln;

%% Create initial guess using 'nvecs'
M_init = create_guess('Data', X, 'Num_Factors', R, 'Factor_Generator', 'nvecs');

%% Call the |cp_opt| method
% Here is an example call to the cp_opt method. By default, each iteration
% prints the least squares fit function value (being minimized) and the
% norm of the gradient. 
[M,M0,output] = cp_opt(X, R, 'init', M_init);

%% Check the