.
    </p>

    <p>
      A typical constrained global optimization problem presents an M-dimensional
      hyper-rectangle bounded by A(1:M) <= X(1:M) <= B(1:M), and a scalar-valued
      function F(X).  The task is to find a point X within the hyper-rectangle
      at which the function takes its minimum value.
    </p>

    <p>
      This task is impossible, mathematically and in general.  However, the problems
      that can be solved mathematically are often not the ones encountered in
      real life.