Moment-sos and spectral hierarchies for polynomial optimization on the sphere and quantum de Finetti theorems
Alexander Taveira Blomenhofer, Monique Laurent

TL;DR
This paper analyzes convergence rates of two hierarchies for polynomial optimization on the sphere, linking moment-sos and spectral bounds, and introduces a new de Finetti theorem with explicit constants.
Contribution
It establishes the limits of spectral bounds convergence, introduces a novel banded de Finetti theorem, and improves existing bounds using the polynomial kernel method.
Findings
Spectral bounds cannot converge faster than O(1/r^2).
The paper introduces a new banded de Finetti theorem for real matrices.
Improves the convergence rate for real maximally symmetric matrices to O(1/r^2).
Abstract
We revisit the convergence analysis of two approximation hierarchies for polynomial optimization on the unit sphere. The first one is based on the moment-sos approach and gives semidefinite bounds for which Fang and Fawzi (2021) showed an analysis in for the r-th level bound, using the polynomial kernel method. The second hierarchy was recently proposed by Lovitz and Johnston (2023) and gives spectral bounds for which they show a convergence rate in , using a quantum de Finetti theorem of Christandl et al. (2007) that applies to complex Hermitian matrices with a "double" symmetry. We investigate links between these approaches, in particular, via duality of moments and sums of squares. Our main results include showing that the spectral bounds cannot have a convergence rate better than and that they do not enjoy generic finite convergence. In addition, we…
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