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Tensor network (TN) methods are well established for computing partition functions in statistical mechanics, though this use has traditionally been limited to lattice models. We extend the scope of TN methodology to interacting particle…

Statistical Mechanics · Physics 2026-04-29 Gunhee Park , Tomislav Begušić , Si-Jing Du , Johnnie Gray , Garnet Kin-Lic Chan

The hierarchical SVD provides a quasi-best low rank approximation of high dimensional data in the hierarchical Tucker framework. Similar to the SVD for matrices, it provides a fundamental but expensive tool for tensor computations. In the…

Numerical Analysis · Mathematics 2017-10-25 Benjamin Huber , Reinhold Schneider , Sebastian Wolf

Singular value decomposition (SVD) has a crucial role in model order reduction. It is often utilized in the offline stage to compute basis functions that project the high-dimensional nonlinear problem into a low-dimensionsl model which is,…

Numerical Analysis · Mathematics 2016-11-09 Alessandro Alla , J. Nathan Kutz

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

Efficient and fast computation of a tensor singular value decomposition (t-SVD) with a few passes over the underlying data tensor is crucial because of its many potential applications. The current/existing subspace randomized algorithms…

Numerical Analysis · Mathematics 2025-02-10 Salman Ahmadi-Asl , Anh-Huy Phan , Andrzej Cichocki

This paper introduces the functional tensor singular value decomposition (FTSVD), a novel dimension reduction framework for tensors with one functional mode and several tabular modes. The problem is motivated by high-order longitudinal data…

Methodology · Statistics 2023-10-27 Rungang Han , Pixu Shi , Anru R. Zhang

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model…

Machine Learning · Computer Science 2018-12-03 Longhao Yuan , Chao Li , Danilo Mandic , Jianting Cao , Qibin Zhao

We present our progress on a study of the $O(3)$ model in two-dimensions using the Tensor Renormalization Group method. We first construct the theory in terms of tensors, and show how to construct $n$-point correlation functions. We then…

High Energy Physics - Lattice · Physics 2014-11-18 Judah Unmuth-Yockey , Yannick Meurice , James Osborn , Haiyuan Zou

We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field…

High Energy Physics - Lattice · Physics 2023-01-12 Muhammad Asaduzzaman , Simon Catterall , Yannick Meurice , Ryo Sakai , Goksu Can Toga

We introduce a broad lemma, one consequence of which is the higher order singular value decomposition (HOSVD) of tensors defined by DeLathauwer, DeMoor and Vandewalle (2000). By an analogous application of the lemma, we find a complex…

Quantum Physics · Physics 2025-10-16 Luke Oeding , Ian Tan

The Canonical Polyadic decomposition (CPD) is a convenient and intuitive tool for tensor factorization; however, for higher-order tensors, it often exhibits high computational cost and permutation of tensor entries, these undesirable…

Numerical Analysis · Computer Science 2018-09-05 Anh-Huy Phan , Andrzej Cichocki , Ivan Oseledets , Salman Ahmadi Asl , Giuseppe Calvi , Danilo Mandic

Consider the partition function of a classical system in two spatial dimensions, or the Euclidean path integral of a quantum system in two space-time dimensions, both on a lattice. We show that the tensor network renormalization (TNR)…

Strongly Correlated Electrons · Physics 2016-01-29 Glen Evenbly , Guifre Vidal

The tensor renormalization group is a promising complementary approach to traditional Monte Carlo methods for lattice systems, as it is inherently free from the sign problem. We discuss recent developments crucial for its application to…

High Energy Physics - Lattice · Physics 2025-01-27 Atis Yosprakob

We study the renormalization group flow of $\phi^4$ theory in two dimensions. Regularizing space into a fine-grained lattice and discretizing the scalar field in a controlled way, we rewrite the partition function of the theory as a tensor…

Strongly Correlated Electrons · Physics 2020-08-26 Clement Delcamp , Antoine Tilloy

For each lattice one can define a free boson theory propagating on the corresponding torus. We give an alternative definition where one employs any automorphism of the group $M^*/M$. This gives a wealth of conformal data, which we realize…

High Energy Physics - Theory · Physics 2009-10-31 Ernest Baver , Doron Gepner , Umut Gursoy

The behavior of dimensionless quantities defined as ratios of partition functions is analyzed to investigate phase transitions and critical phenomena. At criticality, the universal values of these ratios can be predicted from conformal…

Statistical Mechanics · Physics 2026-03-05 Satoshi Morita , Naoki Kawashima

The differential p_T spectrum for vector boson production is computed at next-to-leading fixed order and including the resummation of threshold logarithms at next-to-next-to-leading logarithmic accuracy. A comparison is made to ATLAS data…

High Energy Physics - Phenomenology · Physics 2013-08-08 Thomas Becher , Christian Lorentzen , Matthew D. Schwartz

We present a tensor-network approach for two-dimensional strong-coupling QCD with staggered quarks at nonzero chemical potential. After integrating out the gauge fields at infinite coupling, the partition function can be written as a full…

High Energy Physics - Lattice · Physics 2022-12-28 Jacques Bloch , Robert Lohmayer

We revisit the exact solution of the two space-time dimensional quantum field theory of a free massless boson with a periodic boundary interaction and self-dual period. We analyze the model by using a mapping to free fermions with a…

High Energy Physics - Theory · Physics 2008-11-26 M. Hasselfield , Taejin Lee , G. W. Semenoff , P. C. E. Stamp

In this paper we focus on the problem of completion of multidimensional arrays (also referred to as tensors) from limited sampling. Our approach is based on a recently proposed tensor-Singular Value Decomposition (t-SVD) [1]. Using this…

Machine Learning · Computer Science 2015-03-02 Zemin Zhang , Shuchin Aeron