# Example 6.2: Robust regression using the Huber penalty

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    % Section 6.1.2, Figure 6.5
    % Boyd & Vandenberghe "Convex Optimization"
    % Original by Lieven Vandenberghe
    % Adapted for CVX by Joelle Skaf - 09/07/05
    %
    % Compares the solution of regular Least-squares:
    %           minimize    sum(y_i - alpha - beta*t_i)^2
    % to the solution of the following:
    %           minimize    sum( phi_h (y_i - alpha - beta*t_i)^2 )
    % where phi_h is the Huber penalty function, (t_i,y_i) are data points in a
    % plane.

    % Input data
    randn('seed',1);
    rand('seed',1);

    m=40;  n=2;    A = randn(m,n);
    xex = [5;1];
    pts = -10+20*rand(m,1);
    A = [ones(m,1) pts];
    b = A*xex + .5*randn(m,1);
    outliers = [-9.5; 9];  outvals = [20; -15];
    A = [A; ones(length(outliers),1), outliers];
    b = [b; outvals];
    m = size(A,1);
    pts = [pts;outliers];

    % Least Squares
    fprintf(1,'Computing the solution of the least-squares problem...');

    xls =  A\b;

    fprintf(1,'Done! \n');

    % Huber
    fprintf(1,'Computing the solution of the huber-penalized problem...');

    cvx_begin quiet
        variable xhub(n)
        minimize(sum(huber(A*xhub-b)))
    cvx_end

    fprintf(1,'Done! \n');

    % Plots
    figure(1);  hold off
    plot(pts,b,'o', [-11; 11], [1 -11; 1 11]*xhub, '-', ...
         [-11; 11], [1 -11; 1 11]*xls, '--');
    axis([-11 11 -20 25])
    title('Least-square fit vs robust least-squares fit (Huber-penalized)');
    xlabel('x');
    ylabel('y');
    legend('Data points','Huber penalty','Regular LS','Location','Best');
    %print -deps robustls.eps

    Computing the solution of the least-squares problem...Done!
    Computing the solution of the huber-penalized problem...Done!