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Tensor decomposition serves as a powerful primitive in statistics and machine learning, and has numerous applications in problems such as learning latent variable models or mixture of Gaussians. In this paper, we focus on using power…

Machine Learning · Computer Science 2025-03-25 Yuchen Wu , Kangjie Zhou

Accurate wave-function descriptions of pristine and defected solids remain challenging due to the simultaneous presence of finite-size, basis-set, and correlation errors. While embedding techniques alleviate finite-size effects and…

We introduce a method for accurate quantum chemical calculations based on a simple variational wave function, defined by a single geminal that couples all the electrons into singlet pairs, combined with a real space correlation factor. The…

Materials Science · Physics 2009-10-22 M. Marchi , S. Azadi , M. Casula , S. Sorella

Tensor decomposition is an important technique for capturing the high-order interactions among multiway data. Multi-linear tensor composition methods, such as the Tucker decomposition and the CANDECOMP/PARAFAC (CP), assume that the complex…

Machine Learning · Statistics 2016-11-04 Bin Liu , Zenglin Xu , Yingming Li

Tensor decomposition methods are popular tools for learning latent variables given only lower-order moments of the data. However, the standard assumption is that we have sufficient data to estimate these moments to high accuracy. In this…

Machine Learning · Statistics 2019-03-13 Omer Gottesman , Weiwei Pan , Finale Doshi-Velez

Closed-form expressions for all matrix elements required for variational calculation of the electronic structure of periodic solids have been derived using a basis of explicitly correlated Gaussians (ECGs). Periodic basis functions are…

Quantum Physics · Physics 2026-05-14 Kalman Varga

Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we…

Chemical Physics · Physics 2025-09-25 Hossein Tahmasbi , Michael Beerbaum , Bartosz Brzoza , Attila Cangi , Thomas D. Kühne

We demonstrate that a conditional wavefunction theory enables a unified and efficient treatment of the equilibrium structure and nonadiabatic dynamics of correlated electron-ion systems. The conditional decomposition of the many-body…

Chemical Physics · Physics 2021-07-22 Guillermo Albareda , Kevin Lively , Shunsuke A. Sato , Aaron Kelly , Angel Rubio

Bipolaron energies are calculated as a function of wave vector by a variational method of Gurari appropriate for weak or intermediate coupling strengths, for a model with electron-phonon interactions independent of phonon wave vectors and a…

Superconductivity · Physics 2009-11-11 D. M. Eagles , R. M. Quick , B. Schauer

We present an algorithm for low rank decomposition of tensors of any symmetry type, from fully asymmetric to fully symmetric. It recovers the decomposition one summand at a time via the higher-order power method. This approach is known to…

Numerical Analysis · Mathematics 2026-05-22 Kexin Wang , João M. Pereira , Joe Kileel , Anna Seigal

Tensor decompositions such as the canonical format and the tensor train format have been widely utilized to reduce storage costs and operational complexities for high-dimensional data, achieving linear scaling with the input dimension…

Numerical Analysis · Mathematics 2020-02-11 Oscar Mickelin , Sertac Karaman

We consider the problem of learning mixtures of generalized linear models (GLM) which arise in classification and regression problems. Typical learning approaches such as expectation maximization (EM) or variational Bayes can get stuck in…

Machine Learning · Computer Science 2016-01-14 Hanie Sedghi , Majid Janzamin , Anima Anandkumar

The potential of mean-field decomposition techniques in interpreting electronic transitions in molecules is explored, particularly, the usefulness of these for offering computational signatures of different classes of such excitations. When…

Chemical Physics · Physics 2022-02-14 Janus J. Eriksen

An alternative multipole expansion of the correlation term is derived. Modified spherical Bessel type functions which simplify as a summation of multiple orders of basic trigonometric functions are generated from this new method. We use…

Quantum Physics · Physics 2017-04-10 Eric Ouma Jobunga

In this paper we study sums of powers of affine functions in (mostly) one variable. Although quite simple, this model is a generalization of two well-studied models: Waring decomposition and sparsest shift. For these three models there are…

Computational Complexity · Computer Science 2017-10-25 Ignacio Garcia-Marco , Pascal Koiran , Timothée Pecatte

In this paper, we investigate the energy decay of hyperbolic systems of wave-wave, wave-Euler- Bernoulli beam and beam-beam types. The two equations are coupled through boundary connection with only one localized non-smooth fractional…

Analysis of PDEs · Mathematics 2021-05-18 Mohammad Akil , Ibtissam Issa , Ali Wehbe

In this thesis a general relativistic framework for the calculation of the electroweak structure of mesons of arbitrary constituent-quark masses is presented. The physical processes in which the structure is measured, i.e. electron-meson…

High Energy Physics - Phenomenology · Physics 2013-06-07 María Gómez-Rocha

The Witt ring of symmetric bilinear forms over a field has divided power operations. On the other hand, it follows from Garibaldi-Merkurjev-Serre's work on cohomological invariants that all operations on the Witt ring are essentially linear…

K-Theory and Homology · Mathematics 2023-09-11 Burt Totaro

We recently found that the electromagnetic scattering problem can be very fast in an approach expressing the fields in terms of orthonormal basis functions. In this paper we apply computational conformal geometry with the conformal energy…

Optics · Physics 2025-12-19 Pengcheng Wan , Zhong-Heng Tan , S. T. Chui , Tiexiang Li , S. T. Yau

It is well known that tensor network regression models operate on an exponentially large feature space, but questions remain as to how effectively they are able to utilize this space. Using a polynomial featurization, we propose the…

Machine Learning · Computer Science 2023-01-27 Ian Convy , K. Birgitta Whaley
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