# Euclidean projection on the semidefinite cone

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    % Sec. 8.1.1, Boyd & Vandenberghe "Convex Optimization"
    % Joelle Skaf - 10/07/05
    %
    % The projection of X0 on the proper cone K = S+^n is given by
    %           minimize    ||X - X0||_F
    %               s.t.    X >=0
    % where X is a nxn matrix and ||.||_F is the Frobenius norm
    % It is also given by: P_K(X0)_k = sum_{i=1}^{n}max{0,lam_i}v_i*v_i'
    % s.t. X0= sum_{i=1}^{n}lam_i*v_i*v_i'is the eigenvalue decomposition of X0

    % Input data
    randn('seed',0);
    n  = 10;
    X0 = randn(n);
    X0 = 0.5 * (X0 + X0');
    [V,lam] = eig(X0);

    fprintf(1,'Computing the analytical solution...');
    % Analytical solution
    pk_X0 = V*max(lam,0)*V';
    fprintf(1,'Done! \n');

    % Solution via CVX
    fprintf(1,'Computing the optimal solution by solving an SDP...');

    cvx_begin sdp quiet
        variable X(n,n) symmetric
        minimize ( norm(X-X0,'fro') )
        X >= 0;
    cvx_end

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

    % Verification
    disp('-----------------------------------------------------------------');
    disp('Verifying that the analytical solution and the solution obtained ');
    disp('via CVX are equal by computing ||X_star - P_K(X0)||_F: ');
    norm(X-pk_X0,'fro')
    disp('Hence X_star and P_K(X0) are equal to working precision.');

    Computing the analytical solution...Done!
    Computing the optimal solution by solving an SDP...Done!
    -----------------------------------------------------------------
    Verifying that the analytical solution and the solution obtained
    via CVX are equal by computing ||X_star - P_K(X0)||_F:

    ans =

       2.0864e-05

    Hence X_star and P_K(X0) are equal to working precision.