  Symmetry in mathematical programming may lead to a multiplicity of solutions.
In nonconvex optimisation, it can negatively affect the performance of the
branch-and-bound algorithm. Symmetry may induce large search trees with
multiple equivalent solutions, i.e. with the same optimal value. Dealing with
symmetry requires detecting and classifying it first. This work develops
methods for detecting groups of symmetry in the formulation of quadratically
constrained quadratic optimisation problems via adjacency matrices. Using graph
theory, we transform these matrices into Binary Layered Graphs (BLG) and enter
them into the software package nauty. Nauty generates important symmetric
properties of the original problem.
