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: example_L21.m
    path of file: ./repos/MALSAR/examples
    
    the code of the file until where you have to start completion: %% file example_L21.m
% this file shows the usage of Least_L21.m function 
% and study the group sparsity patterns. 
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2) 
%            + rho1 * \|W\|_{2,1} + opts.rho_L2 * \|W\|_F^2}
%
%% LICENSE
%   This program is free software: you can redistribute it and/or modify
%   it under the terms of the GNU General Public License as published by
%   the Free Software Foundation, either version 3 of the License, or
%   (at your option) any later version.
%
%   This program is distributed in the hope that it will be useful,
%   but WITHOUT ANY WARRANTY; without even the implied warranty of
%   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%   GNU General Public License for more details.
%
%   You should have received a copy of the GNU General Public License
%   along with this program.  If not, see <http://www.gnu.org/licenses/>.
%
%   Copyright (C) 2011 - 2012 Jiayu Zhou and Jieping Ye 
%
%   You are suggested to first read the Manual.
%   For any problem, please contact with Jiayu Zhou via jiayu.zhou@asu.edu
%
%   Last modified on June 3, 2012.
%
%% Related papers
%
% [1] Evgeniou, A. and Pontil, M. Multi-task feature learning, NIPS 2007.
% [2] Liu, J. and Ye, J. Efficient L1/Lq Norm Regularization, Technical
% Report, 2010.
%

clear;
clc;
close;

addpath('../MALSAR/functions/joint_feature_learning/'); % load function
addpath('../MALSAR/utils/'); % load utilities

load('../data/school.mat'); % load sample data.
d = size(X{1}, 2);  % dimensionality.

lambda = [200 :300: 1500];

%rng('default');     % reset random generator. Available from Matlab 2011.
opts.init = 0;      % guess start point from data. 
opts.tFlag = 1;     % terminate after relative objectiv
    
function 
% and study the group sparsity patterns. 
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2) 
%            + rho1 * \|W\|_{2,1} + opts.rho_L2 * \|W\|_F^2}
%
%% LICENSE
%   This program is free software: you can redistribute it and/or modify
%   it under the terms of the GNU General Public License as published by
%   the Free Software Foundation, either version 3 of the License, or
%   (at your option) any later version.
%
%   This program is distributed in the hope that it will be useful,
%   but WITHOUT ANY WARRANTY; without even the implied warranty of
%   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%   GNU General Public License for more details.
%
%   You should have received a copy of the GNU General Public License
%   along with this program.  If not, see <http://www.gnu.org/licenses/>.
%
%   Copyright (C) 2011 - 2012 Jiayu Zhou and Jieping Ye 
%
%   You are suggested to first read the Manual.
%   For any problem, please contact with Jiayu Zhou via jiayu.zhou@asu.edu
%
%   Last modified on June 3, 2012.
%
%% Related papers
%
% [1] Evgeniou, A. and Pontil, M. Multi-task feature learning, NIPS 2007.
% [2] Liu, J. and Ye, J. Efficient L1/Lq Norm Regularization, Technical
% Report, 2010.
%

clear;
clc;
close;

addpath('../MALSAR/functions/joint_feature_learning/'); % load function
addpath('../MALSAR/utils/'); % load utilities

load('../data/school.mat'); % load sample data.
d = size(X{1}, 2);  % dimensionality.

lambda = [200 :300: 1500];

%rng('default');     % reset random generator. Available from Matlab 2011.
opts.init = 0;      % guess start point from data. 
opts.tFlag = 1;     % terminate after relative objective value does not changes much.
opts.tol = 10^-5;   % tolerance. 
opts.maxIter = 1000; % maximum iteration number of optimization.

sparsity = zeros(length(lambda), 1);
log_lam  = log(lambda);

for i = 1: length(lambda)
    [W funcVal] = Least_L21(X, Y, lambda(i), opts);
    % set the solution as the next initial point. 
    % this gives better efficiency. 
    opts.init = 1;
    opts.W0 = W;
    sparsity(i) = nnz(sum(W,2 )==0)/d;
end