You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: Experiment 2.m
    path of file: ./repos/Numerical-Linear-Algebra/9. MATLAB
    
    the code of the file until where you have to start completion: function [] = Experiment2()
%% First, we construct a square A with random singular vectors and 
%% widely varying singular values spaced by factors of 2 between 2^-1 and 2^-80
[U, ~] = qr(randn(80)); % Set U to a random orthogonal matrix
[V, ~] = qr(randn(80)); % Set V to a random orthogonal matrix
S = diag(2.^(-1:-1:-80)); % Set S to a diagonal matrix with exponentially graded entries
A = U * S * V; % Set A to a matrix with these entries as singular values
[m, n] = size(A);

%% Now, we use 'classical' and 'modified' Gram-Schmidt to compute QR factorization of A.
% classical 

    
function [] = Experiment2()
%% First, we construct a square A with random singular vectors and 
%% widely varying singular values spaced by factors of 2 between 2^-1 and 2^-80
[U, ~] = qr(randn(80)); % Set U to a random orthogonal matrix
[V, ~] = qr(randn(80)); % Set V to a random orthogonal matrix
S = diag(2.^(-1:-1:-80)); % Set S to a diagonal matrix with exponentially graded entries
A = U * S * V; % Set A to a matrix with these entries as singular values
[m, n] = size(A);

%% Now, we use 'classical' and 'modified' Gram-Schmidt to compute QR factorization of A.
% classical 
QC = zeros(m, n); RC = zeros(m, n); VC = A;
for j = 1:n
    for i = 1:j-1
        RC(i, j) = dot(QC(:, i), VC(:, j));
        VC(:, j) = VC(:, j) - RC(i, j) * QC(:, i);
    end