Checking non-negativity of polynomials using sum-of-squares has recently been popularized and found many applications in control. Although the method is based on convex programming, the optimization problems rapidly grow and result in huge semidefinite programs. The paper [4] describes how symmetry is exploited in sum-of-squares problems in the MATLAB toolbox YALMIP, but concentrates on the scalar case. This report serves as an addendum, and extends the strategy to matrix-valued sum-of-squares problems.
