On symmetry adapted bases in trigonometric optimization

TL;DR

This paper introduces a symmetry-adapted basis for trigonometric optimization problems invariant under finite group actions, reducing computational complexity of semidefinite relaxations by exploiting invariance and block-diagonalization.

Contribution

It presents a novel approach that constructs a symmetry-adapted basis to exploit group invariance, simplifying SDP computations in trigonometric optimization.

Findings

Reduced SDP complexity through block-diagonalization

Established a symmetry-adapted basis for group-invariant problems

Demonstrated the approach's generality in trigonometric optimization

Abstract

The problem of computing the global minimum of a trigonometric polynomial is computationally hard. We address this problem for the case, where the polynomial is invariant under the exponential action of a finite group. The strategy is to follow an established relaxation strategy in order to obtain a converging hierarchy of lower bounds. Those bounds are obtained by numerically solving semi-definite programs (SDPs) on the cone of positive semi-definite Hermitian Toeplitz matrices, which is outlined in the book of Dumitrescu [Dum07]. To exploit the invariance, we show that the group has an induced action on the Toeplitz matrices and prove that the feasible region of the SDP can be restricted to the invariant matrices, whilst retaining the same solution. Then we construct a symmetry adapted basis tailored to this group action, which allows us to block-diagonalize invariant matrices and thus reduce the computational complexity to solve the SDP. The approach is in its generality novel for trigonometric optimization and complements the one that was proposed as a poster at the ISSAC 2022 conference [HMMR22] and later extended to [HMMR24]. In the previous work, we first used the invariance of the trigonometric polynomial to obtain a classical polynomial optimization problem on the orbit space and subsequently relaxed the problem to an SDP. Now, we first make the relaxation and then exploit invariance. Partial results of this article have been presented as a poster at the ISSAC 2023 conference [Met23].