function sse=sse(param)
%-----------------------------------------------------------------------
% Evaluate the sum of squared error (sse), a scalar quantity, between data
% point and a given function (which satisfies the following one-dimensional
% diffusion-reaction equation.
%     Diffusion = Reaction
%
%   (d^2y/dx^2) = f(y)    BC: at x=0, dy/dx=0
%                             at x=1, y=1
%
% where f is a function for saturation (Michaelis-Menten) kinetics
%     f     = alpha*y(i)/(beta+y(i))
%     alpha = input to this function
%     beta  = input to this function
%
% This function is called by finitedr.m
%-----------------------------------------------------------------------
% Instructor: Nam Sun Wang
%-----------------------------------------------------------------------

% Although xDataz is not used in this specific example; however, it is
% generally needed when the noisy test data points are not artificially
% generated but are experimentally obtained.  Likewise, xDataz is needed
% when the number of test data points and the number of point used to
% represent the given function y are not identical.
      global xDataz yDataz

% Use my own variables
      alpha = param(1);
      beta  = param(2);

% The given function with the supplied parameters
      [x, y] = finitedb(alpha, beta);

% Find the error between data points and the given function
      error = yDataz - y;
      sse = sum(error.^2);