function f = mckinnon ( x )

%*****************************************************************************80
%
%% MCKINNON computes the McKinnon function.
%
%  Discussion:
%
%    This function has a global minimizer:
%
%      X* = ( 0.0, -0.5 ), F(X*) = -0.25.
%
%    There are three parameters, TAU, THETA and PHI.
%
%    1 < TAU, then F is strictly convex.
%             and F has continuous first derivatives.
%    2 < TAU, then F has continuous second derivatives.
%    3 < TAU, then F has continuous third derivatives.
%
%    However, this function can cause the Nelder-Mead optimization
%    algorithm to "converge" to a point which is not the minimizer
%    of the function F.
%
%    Sample parameter values which cause problems for Nelder-Mead 
%    include:
%
%      TAU = 1, THETA = 15, PHI =  10;
%      TAU = 2, THETA =  6, PHI =  60;
%      TAU = 3, THETA =  6, PHI = 400;
%
%    To get the bad behavior, we also assume the initial simplex has the form
%
%      X1 = (0,0),
%      X2 = (1,1),
%      X3 = (A,B), 
%
%    where 
%
%      A = (1+sqrt(33))/8 =  0.84307...
%      B = (1-sqrt(33))/8 = -0.59307...
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    01 January 2011
%
%  Author:
%
%    John Burkardt
%
%  Reference:
%
%    Ken McKinnon,
%    Convergence of the Nelder-Mead simplex method to a nonstationary point,
%    SIAM Journal on Optimization,
%    Volume 9, Number 1, 1998, pages 148-158.
%
%  Parameters:
%
%    Input, real X(2), the argument of the function.
%
%    Output, real F, the value of the function at X.
%
  if ( length ( x ) ~= 2 )
    error ( 'Error: function expects a two dimensional input\n' );
  end

  tau = 2.0;
  theta = 6.0;
  phi = 60.0;

  if ( x(1) <= 0.0 )
    f = theta * phi * abs ( x(1) ).^tau + x(2) * ( 1.0 + x(2) );
  else
    f = theta       *       x(1).^tau   + x(2) * ( 1.0 + x(2) );
  end

  return
end