English

The capacity of quiver representations and Brascamp-Lieb constants

Representation Theory 2021-04-26 v3 Classical Analysis and ODEs

Abstract

Let QQ be a bipartite quiver, VV a real representation of QQ, and σ\sigma an integral weight of QQ orthogonal to the dimension vector of VV. Guided by quiver invariant theoretic considerations, we introduce the Brascamp-Lieb operator TV,σT_{V,\sigma} associated to (V,σ)(V,\sigma) and study its capacity, denoted by DQ(V,σ)\mathbf{D}_Q(V, \sigma). When QQ is the mm-subspace quiver, the capacity of quiver data is intimately related to the Brascamp-Lieb constants that occur in the mm-multilinear Brascamp-Lieb inequality in analysis. We show that the positivity of DQ(V,σ)\mathbf{D}_Q(V, \sigma) is equivalent to the σ\sigma-semi-stability of VV. We also find a character formula for DQ(V,σ)\mathbf{D}_Q(V, \sigma) whenever it is positive. Our main tool is a quiver version of a celebrated result of Kempf-Ness on closed orbits in invariant theory. This result leads us to consider certain real algebraic varieties that carry information relevant to our main objects of study. It allows us to express the capacity of quiver data in terms of the character induced by σ\sigma and sample points of the varieties involved. Furthermore, we use this character formula to prove a factorization of the capacity of quiver data. We also show that the existence of gaussian extremals for (V,σ)(V, \sigma) is equivalent to VV being σ\sigma-polystable, and that the uniqueness of gaussian extremals implies that VV is σ\sigma-stable. Finally, we explain how to find the gaussian extremals of a gaussian-extremisable datum (V,σ)(V, \sigma) using the algebraic variety associated to (V,σ)(V,\sigma).

Keywords

Cite

@article{arxiv.1905.04783,
  title  = {The capacity of quiver representations and Brascamp-Lieb constants},
  author = {Calin Chindris and Harm Derksen},
  journal= {arXiv preprint arXiv:1905.04783},
  year   = {2021}
}

Comments

v3: minor corrections; v2:minor typos fixed; the statement on gaussian-extremisable quiver data strengthen