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Related papers: Bootstrapping Tensor Integrals

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Given a matrix model, by combining the Schwinger-Dyson equations with positivity constraints on its solutions, in the large $N$ limit one is able to obtain explicit and numerical bounds on its moments. This technique is known as…

Mathematical Physics · Physics 2025-02-27 Masoud Khalkhali , Nathan Pagliaroli , Andrei Parfeni , Brayden Smith

We apply the bootstrap technique to find the moments of certain multi-trace and multi-matrix random matrix models suggested by noncommutative geometry. Using bootstrapping we are able to find the relationships between the coupling constant…

High Energy Physics - Theory · Physics 2022-02-09 Hamed Hessam , Masoud Khalkhali , Nathan Pagliaroli

We explore how matrix bootstrap techniques can be used to constrain matrix and tensor models at finite $N$, where $N$ is the dimension of the matrix/tensor, taking a Gaussian model with a quartic interaction as example. For matrix models,…

High Energy Physics - Theory · Physics 2026-05-04 Samuel Laliberte , Reiko Toriumi

We show that bootstrap methods based on the positivity of probability measures provide a systematic framework for studying both synchronous and asynchronous nonequilibrium stochastic processes on infinite lattices. First, we formulate…

Statistical Mechanics · Physics 2025-11-12 Minjae Cho

We propose the relaxation bootstrap method for the numerical solution of multi-matrix models in the large $N$ limit, developing and improving the recent proposal of H.Lin. It gives rigorous inequalities on the single trace moments of the…

High Energy Physics - Theory · Physics 2022-06-22 Vladimir Kazakov , Zechuan Zheng

We develop a bootstrap approach to Euclidean two-point correlators, in the thermal or ground state of quantum mechanical systems. We formulate the problem of bounding the two-point correlator as a semidefinite programming problem, subject…

High Energy Physics - Theory · Physics 2026-04-08 Minjae Cho , Barak Gabai , Henry W. Lin , Jessica Yeh , Zechuan Zheng

We consider matrix quantum mechanics with multiple bosonic matrices, including those obtained from dimensional reduction of Yang-Mills theories. Using the matrix bootstrap, we study simple observables like $\langle \mathop{tr} X^2 \rangle$…

High Energy Physics - Theory · Physics 2025-09-22 Henry W. Lin , Zechuan Zheng

Bootstrapping can produce confidence levels for hypotheses about quadratic regression models - such as whether the U-shape is inverted, and the location of optima. The method has several advantages over conventional methods: it provides…

Methodology · Statistics 2012-07-09 Michael Wood

Using numerical bootstrap method, we determine the critical exponents of the minimal three-dimensional N = 1 Wess-Zumino models with cubic superpotetential $W\sim d_{ijk}\Phi_i\Phi_j\Phi_k$. The tensor $d_{ijk}$ is taken to be the invariant…

High Energy Physics - Theory · Physics 2019-10-22 Junchen Rong , Ning Su

Bootstrap methods, initially developed for solving statistical and quantum field theories, have recently been shown to capture the discrete spectrum of quantum mechanical problems, such as the single particle Schr\"odinger equation with an…

Mesoscale and Nanoscale Physics · Physics 2021-12-15 Serguei Tchoumakov , Serge Florens

Large $N$ matrix quantum mechanics is central to holographic duality but not solvable in the most interesting cases. We show that the spectrum and simple expectation values in these theories can be obtained numerically via a `bootstrap'…

High Energy Physics - Theory · Physics 2020-07-29 Xizhi Han , Sean A. Hartnoll , Jorrit Kruthoff

We study multiscalar theories with $\text{O}(N) \times \text{O}(2)$ symmetry. These models have a stable fixed point in $d$ dimensions if $N$ is greater than some critical value $N_c(d)$. Previous estimates of this critical value from…

High Energy Physics - Theory · Physics 2025-02-19 Marten Reehorst , Slava Rychkov , Benoit Sirois , Balt C. van Rees

We introduce Wedge Sampling, a new non-adaptive sampling scheme for low-rank tensor completion. We study recovery of an order-$k$ low-rank tensor of dimension $n \times \cdots \times n$ from a subset of its entries. Unlike the standard…

Machine Learning · Statistics 2026-02-06 Hengrui Luo , Anna Ma , Ludovic Stephan , Yizhe Zhu

In this review, we aim to utilize the bootstrap method to study models that have received significant interest in high energy theory and holography recently. Matrix bootstrap is proposed to determine the range of the solution up to an…

General Relativity and Quantum Cosmology · Physics 2026-05-19 Shu Luo

In this paper, we present a technically simple method to establish upper bounds on the expected injective norm of real and complex random tensors. Our approach is somewhat analogous to the moment method in random matrix theory, and is based…

Probability · Mathematics 2026-03-03 Stephane Dartois , Benjamin McKenna

Accurate contraction of tensor networks beyond one dimension is essential in various fields including quantum many-body physics. Existing approaches typically rely on approximate contraction schemes and do not provide certified error bars.…

Strongly Correlated Electrons · Physics 2026-03-19 Seishiro Ono , Yanbai Zhang , Hoi Chun Po

This thesis focuses on renormalization of quantum field theories. Its first part considers three tensor models in three dimensions, a Fermionic quartic with tensors of rank-3 and two Bosonic sextic, of ranks 3 and 5. We rely upon the…

High Energy Physics - Theory · Physics 2020-10-16 Nicolas Delporte

We use the numerical conformal bootstrap to study boundary quantum electrodynamics, the theory of a four dimensional photon in a half space coupled to charged conformal matter on the boundary. This system is believed to be a boundary…

High Energy Physics - Theory · Physics 2023-12-14 Samuel Bartlett-Tisdall , Christopher P. Herzog , Vladimir Schaub

When faced with severely imbalanced binary classification problems, we often train models on bootstrapped data in which the number of instances of each class occur in a more favorable ratio, e.g., one. We view algorithmic inequity through…

Machine Learning · Statistics 2021-08-17 Harish S. Bhat , Majerle E. Reeves , Sidra Goldman-Mellor

We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because the number of relevant constraints may be on the same order…

Methodology · Statistics 2024-02-19 Nils Sturma , Mathias Drton , Dennis Leung
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