   In this talk we present a method for finding exact solutions of the Max-Cut
problem (MC), an NP-hard combinatorial optimization problem. This method is based
 on a relaxation of MC using Semidefinite Programming (SDP). We use a branch and
  bound setting, that applies the basic SDP bound strengthened by the so-called
 triangle inequalities. This upper bound is computed by a dynamic version of the
  bundle method to obtain a "nearly optimal" and "nearly feasible" solution. \\
Besides presenting in detail this bounding routine, we also discuss some branching
rules. Furthermore, we review other solution approaches and compare the numerical
results with our method. We also extend our experiments to unconstrained quadratic
0-1 problems. The experiments show, that our method nearly always outperforms all
other approaches. In particular, where Linear Programming based methods fail (i.e.
  dense graphs) our method performs very well. Exact solutions are obtained in
  reasonable time for any instance of size up to n=100 vertices, no matter what
density. We could prove optimality for several problems of the literature where,
         to the best of our knowledge, no other method is able to do so.