Symmetry Adapted Bases for Trigonometric Optimization

We present an algorithm to compute the global minimum of a trigonometric polynomial, when it is invariant under the exponential action of a Weyl group. This is based on a common relaxation technique that leads to a semi-definite program (SDP). It is then shown how to exploit the invariance in order to reduce the number of variables of the SDP and to simplify its structure significantly. This approach complements the one that was proposed as a poster at the recent ISSAC 2022 conference [HMMR22] and later extended to [HMMR23]. In the previous work, we first used the invariance of the objective function to obtain a classical polynomial optimization problem on the orbit space and subsequently relaxed the problem to an SDP. In the present work, we first apply the relaxation and then exploit symmetry. We show that the Weyl group action is induced by an orthogonal representation and describe its isotypic decomposition.