Tensor models from the viewpoint of matrix models: the case of the Gaussian distribution

Abstract

Observables in random tensor theory are polynomials in the entries of a tensor of rank $d$ which are invariant under $U(N{)}^d$. It is notoriously difficult to evaluate the expectations of such polynomials, even in the Gaussian distribution. In this article, we introduce singular value decompositions to evaluate the expectations of polynomial observables of Gaussian random tensors. Performing the matrix integrals over the unitary group leads to a notion of effective observables which expand onto regular, matrix trace invariants. Examples are given to illustrate that both asymptotic and exact new calculations of expectations can be performed this way.

arXiv comment9 pages

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Cited by in corpus (2)

• Tensor models from the viewpoint of matrix models: the case of loop models on random surfaces
• New Limits for Large $N$ Matrix and Tensor Models: Large $D$, Melons and Applications