# Example 7.4: Binary hypothesis testing

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    % Figure 7.4
    % Boyd & Vandenberghe "Convex Optimization"
    % Original version by Lieven Vandenberghe
    % Updated for CVX by Michael Grant, 2005-12-19

    % Generate the data
    P = [0.70  0.10
         0.20  0.10
         0.05  0.70
         0.05  0.10];
    [n,m] = size(P);

    % Construct the tradeoff curve by finding the
    % the Pareto optimal deterministic detectors,
    % which are the curve's vertices

    nopts   = 1000;
    weights = logspace(-5,5,nopts);
    obj     = [0;1];
    inds    = ones(n,1);

    % minimize  -t1'*q1 - w*t2'*q2
    % s.t.      t1+t2 = 1,  t1,t2 \geq 0

    next = 2;
    for i = 1 : nopts,
       PW = P * diag( [ 1 ; weights(i) ] );
       [ maxvals, maxinds ] = max( PW' );  % max elt in each row
       if (~isequal(maxinds', inds(:,next-1)))
           inds(:,next) = maxinds';
           T = zeros(m,n);
           for j=1:n
              T(maxinds(1,j),j) = 1;
           end;
           obj(:,next) = 1-diag(T*P);
           next = next+1;
       end;
    end;
    plot(obj(1,:), obj(2,:),[0 1], [0 1],'--');
    grid on
    for i=2:size(obj,2)-1
       text(obj(1,i),obj(2,i),['a', num2str(i-1)]);
    end;

    % Minimax detector: not deterministic

    cvx_begin
        variables T( m, n ) D( m, m )
        minimize max( D(1,2), D(2,1) )
        subject to
            D == T * P;
            sum( T, 1 ) == 1;
            T >= 0;
    cvx_end

    objmp = 1 - diag( D );
    text( objmp(1), objmp(2), 'b' );
    xlabel('P_{fp}'); ylabel('P_{fn}');

    %print -deps roc.eps

    Calling sedumi: 10 variables, 5 equality constraints
    ------------------------------------------------------------
    SeDuMi 1.21 by AdvOL, 2005-2008 and Jos F. Sturm, 1998-2003.
    Alg = 2: xz-corrector, Adaptive Step-Differentiation, theta = 0.250, beta = 0.500
    eqs m = 5, order n = 11, dim = 11, blocks = 1
    nnz(A) = 18 + 0, nnz(ADA) = 13, nnz(L) = 9
     it :     b*y       gap    delta  rate   t/tP*  t/tD*   feas cg cg  prec
      0 :            3.84E+00 0.000
      1 :   4.94E-01 1.11E+00 0.000 0.2891 0.9000 0.9000   3.24  1  1  7.1E-01
      2 :   2.26E-01 2.18E-01 0.000 0.1960 0.9000 0.9000   1.25  1  1  1.7E-01
      3 :   1.82E-01 5.12E-02 0.000 0.2353 0.9000 0.9000   1.05  1  1  4.6E-02
      4 :   1.68E-01 4.07E-03 0.000 0.0794 0.9900 0.9900   1.01  1  1  4.0E-03
      5 :   1.67E-01 1.65E-06 0.000 0.0004 0.9999 0.9999   1.00  1  1
    iter seconds digits       c*x               b*y
      5      0.0   Inf  1.6666666667e-01  1.6666666667e-01
    |Ax-b| =   1.6e-16, [Ay-c]_+ =   0.0E+00, |x|=  1.9e+00, |y|=  3.5e-01

    Detailed timing (sec)
       Pre          IPM          Post
    0.000E+00    2.000E-02    0.000E+00
    Max-norms: ||b||=1, ||c|| = 1,
    Cholesky |add|=0, |skip| = 0, ||L.L|| = 1.
    ------------------------------------------------------------
    Status: Solved
    Optimal value (cvx_optval): +0.166667