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The rank three tensor model with tetrahedral interaction was shown by Carrozza and Tanasa to admit a $1/N$ expansion, dominated by melonic diagrams, and double tadpoles decorated with melons at next-to-leading order. This model has…

Mathematical Physics · Physics 2019-12-25 Valentin Bonzom

Various tensor models have been recently shown to have the same properties as the celebrated Sachdev-Ye-Kitaev (SYK) model. In this paper we study in detail the diagrammatics of two such SYK-like tensor models: the multi-orientable (MO)…

High Energy Physics - Theory · Physics 2019-09-04 V. Bonzom , V. Nador , A. Tanasa

A graph $U$ is an induced universal graph for a family $F$ of graphs if every graph in $F$ is a vertex-induced subgraph of $U$. For the family of all undirected graphs on $n$ vertices Alstrup, Kaplan, Thorup, and Zwick [STOC 2015] give an…

Data Structures and Algorithms · Computer Science 2016-07-25 Mikkel Abrahamsen , Stephen Alstrup , Jacob Holm , Mathias Bæk Tejs Knudsen , Morten Stöckel

The perturbative expansion of tensorial field theories in Feynman graphs can be interpreted as weighted generating series of some piecewise linear varieties. This simple fact establishes a link between two a priori distinct fields: the…

Combinatorics · Mathematics 2023-12-04 Victor Nador

Colored tensor models (CTM) is a random geometrical approach to quantum gravity. We scrutinize the structure of the connected correlation functions of general CTM-interactions and organize them by boundaries of Feynman graphs. For rank-$D$…

Mathematical Physics · Physics 2020-02-05 Carlos I. Pérez-Sánchez

We examine n component spin systems with arbitrary two spin interactions (of unspecified range) within a general framework to highlight some new subtleties present in incommensurate systems. We determine the ground states of all…

Statistical Mechanics · Physics 2007-05-23 Zohar Nussinov

In this paper, we continue the study of large $N$ problems for the Wick renormalized linear sigma model, i.e. $N$-component $\Phi^4$ model, in two spatial dimensions, using stochastic quantization methods and Dyson--Schwinger equations. We…

Probability · Mathematics 2023-06-29 Hao Shen , Rongchan Zhu , Xiangchan Zhu

We use tensor network techniques to obtain high order perturbative diagrammatic expansions for the quantum many-body problem at very high precision. The approach is based on a tensor train parsimonious representation of the sum of all…

Weingarten functions provide a tool for computing Haar measure matrix integrals of polynomials in the matrix entries. An important property of Weingarten functions, is their particularly simple large $N$ limits. In 2017 Benoit Collins and…

Probability · Mathematics 2026-01-08 Ron Nissim

We consider generalized one-matrix models in which external fields allow control over the coordination numbers on both the original and dual lattices. We rederive in a simple fashion a character expansion formula for these models originally…

High Energy Physics - Theory · Physics 2016-09-06 Vladimir A. Kazakov , Matthias Staudacher , Thomas Wynter

We present a method of calculating the interacting S-matrix to an arbitrary perturbative order for a large class of boson interaction Lagrangians. The method takes advantage of a previously unexplored link between the $n$-point Green's…

High Energy Physics - Theory · Physics 2018-02-09 Kamil Bradler

Random matrix ensembles are introduced that respect the local tensor structure of Hamiltonians describing a chain of $n$ distinguishable spin-half particles with nearest-neighbour interactions. We prove a central limit theorem for the…

Mathematical Physics · Physics 2017-06-19 J. P. Keating , N. Linden , H. J. Wells

In this article we report a preliminary investigation of the large $N$ limit of a generalized one-matrix model which represents an $O(n)$ symmetric model on a random lattice. The model on a regular lattice is known to be critical only for…

High Energy Physics - Theory · Physics 2009-10-22 B. Eynard , J. Zinn-Justin

We analyze in full mathematical rigor the most general quartically perturbed invariant probability measure for a random tensor. Using a version of the Loop Vertex Expansion (which we call the mixed expansion) we show that the cumulants…

Mathematical Physics · Physics 2015-06-15 Razvan Gurau

The large N limit of the hermitian matrix model in three and four Euclidean space-time dimensions is studied with the help of the approximate Renormalization Group recursion formula. The planar graphs contributing to wave function, mass and…

High Energy Physics - Theory · Physics 2009-10-28 Gabriele Ferretti

We search for infrared fixed points of Gross-Neveu Yukawa models with matrix degrees of freedom in $d=4-\varepsilon$. We consider three models -- a model with $SU(N)$ symmetry in which the scalar and fermionic fields both transform in the…

High Energy Physics - Theory · Physics 2024-01-15 Shiroman Prakash , Shubham Kumar Sinha

In this paper we perform the 1/N expansion of the colored three dimensional Boulatov tensor model. As in matrix models, we obtain a systematic topological expansion, with more and more complicated topologies suppressed by higher and higher…

General Relativity and Quantum Cosmology · Physics 2011-05-18 Razvan Gurau

This paper provides an extension of the constructive loop vertex expansion to stable matrix models with interactions of arbitrarily high order. We introduce a new representation for such models, then perform a forest expansion on this…

Mathematical Physics · Physics 2019-03-11 Thomas Krajewski , Vincent Rivasseau , Vasily Sazonov

In N=1 supersymmetric SO(N)/USp(2N) gauge theories with the tree-level superpotential W(\Phi) that is an arbitrary polynomial of the adjoint matter \Phi, the massless fluctuations about each quantum vacuum are described by U(1)^n gauge…

High Energy Physics - Theory · Physics 2010-12-03 Changhyun Ahn , Yutaka Ookouchi

Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential Machines (ExM), a predictor that models…

Machine Learning · Statistics 2017-12-11 Alexander Novikov , Mikhail Trofimov , Ivan Oseledets
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