  This paper propose a new frame work for finding global minima which we call
optimization by cut. In each iteration, it takes some samples from the feasible
region and evaluates the objective function at these points. Based on the
observations it cuts off from the feasible region a subregion that is unlikely
to contain a global minimum. The procedure is then repeated with the feasible
region replaced by the remaining region until the remaining region is ``small''
enough. If a global minimum is kept in the remaining region of each iteration,
then it can be located with an arbitrary precision. The frame work is
surprisingly efficient in view of its simple form and can be applied to
black-box functions since neither special structure nor derivative information
is required. The performance of the proposed frame work is evaluated on some
benchmark functions and the results show that it can find a global minimum
rather quickly.
