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We give a deterministic algorithm that, given a composite number $N$ and a target order $D \ge N^{1/6}$, runs in time $D^{1/2+o(1)}$ and finds either an element $a \in \mathbb{Z}_N^*$ of multiplicative order at least $D$, or a nontrivial…

Data Structures and Algorithms · Computer Science 2025-10-14 Ziv Oznovich , Ben Lee Volk

We examine the relative performance of algorithms for the calculation of curvature in spacetime. The classical coordinate component method is compared to two distinct versions of the Newman-Penrose tetrad approach for a variety of…

General Relativity and Quantum Cosmology · Physics 2009-10-28 Denis Pollney , Peter Musgrave , Kevin Santosuosso , Kayll Lake

Efficient probabilistic inference by variable elimination in graphical models requires an optimal elimination order. However, finding an optimal order is a challenging combinatorial optimisation problem for models with a large number of…

Artificial Intelligence · Computer Science 2025-03-13 Sagad Hamid , Tanya Braun

We show that the coefficients of decomposition into an irreducible components of the tensor powers of level $r$ symmetric algebra of adjoint representation coincide with the Verlinder numbers. Also we construct (for $sl(2)) the…

High Energy Physics - Theory · Physics 2008-02-03 Anatol N. Kirillov

We present an alternating least squares type numerical optimization scheme to estimate conditionally-independent mixture models in $\mathbb{R}^n$, without parameterizing the distributions. Following the method of moments, we tackle an…

Numerical Analysis · Mathematics 2023-08-09 Yifan Zhang , Joe Kileel

We give efficient algorithms for finding power-sum decomposition of an input polynomial $P(x)= \sum_{i\leq m} p_i(x)^d$ with component $p_i$s. The case of linear $p_i$s is equivalent to the well-studied tensor decomposition problem while…

Data Structures and Algorithms · Computer Science 2022-08-02 Mitali Bafna , Jun-Ting Hsieh , Pravesh K. Kothari , Jeff Xu

We propose a new algorithm for tensor decomposition, based on Jennrich's algorithm, and apply our new algorithmic ideas to blind deconvolution and Gaussian mixture models. Our first contribution is a simple and efficient algorithm to…

Machine Learning · Computer Science 2021-02-23 Haolin Chen , Luis Rademacher

Stable computational algorithms for the approximate solution of the Cauchy problem for nonstationary problems are based on implicit time approximations. Computational costs for boundary value problems for systems of coupled multidimensional…

Numerical Analysis · Mathematics 2024-03-28 P. N. Vabishchevich

We consider the problem of decomposing higher-order moment tensors, i.e., the sum of symmetric outer products of data vectors. Such a decomposition can be used to estimate the means in a Gaussian mixture model and for other applications in…

Numerical Analysis · Mathematics 2020-10-06 Samantha Sherman , Tamara G. Kolda

We prove a closed character formula for the symmetric powers $S^N V(\lambda)$ of a fixed irreducible representation $V(\lambda)$ of a complex semi-simple Lie algebra $\mathfrak{g}$ by means of partial fraction decomposition. The formula…

Representation Theory · Mathematics 2010-09-22 Stavros Kousidis

An algorithm is developed for efficiently constructing the Lorentz covariant effective three-point vertices of the decay of a particle into two daughter particles in which all the masses and spins of the three particles can be arbitrary.…

High Energy Physics - Phenomenology · Physics 2022-02-02 Seong Youl Choi , Jae Hoon Jeong

A density-functional-theory based approach to efficiently compute numerically exact vibrational free energies - including anharmonicity - for chemically complex multicomponent alloys is developed. It is based on a combination of…

A new algorithm of the canonical polyadic decomposition (CPD) presented here. It features lower computational complexity and memory usage than the available state of the art implementations. We begin with some examples of CPD applications…

Numerical Analysis · Mathematics 2021-10-13 Felipe Bottega Diniz

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

In this survey, we summarize some results in the literature involving the mesh category, which is a combinatorial representation of the category of modules over a finite-dimensional associative algebra. We discuss Riedtmann's well-behaved…

Representation Theory · Mathematics 2025-07-08 Viktor Chust , Flávio U. Coelho

The popular Alternating Least Squares (ALS) algorithm for tensor decomposition is efficient and easy to implement, but often converges to poor local optima---particularly when the weights of the factors are non-uniform. We propose a…

Machine Learning · Computer Science 2017-09-26 Vatsal Sharan , Gregory Valiant

String diagrams are an increasingly popular algebraic language for the analysis of graphical models of computations across different research fields. Whereas string diagrams have been thoroughly studied as semantic structures, much less…

Category Theory · Mathematics 2022-11-04 Paul Wilson , Fabio Zanasi

We introduce a nonsymmetric, associative tensor product among representations of Cuntz algebras by using embeddings. We show the decomposition formulae of tensor products for permutative representations explicitly We apply decomposition…

Operator Algebras · Mathematics 2007-05-23 Katsunori Kawamura

For a finite $\mathbb{Z}$-algebra $R$, i.e., for a ring which is not necessarily associative or unitary, but whose additive group is finitely generated, we construct a decomposition of $R/{\rm Ann}(R)$ into directly indecomposable factors…

Rings and Algebras · Mathematics 2023-08-04 Martin Kreuzer , Alexei Miasnikov , Florian Walsh

The widespread use of multi-sensor technology and the emergence of big datasets has highlighted the limitations of standard flat-view matrix models and the necessity to move towards more versatile data analysis tools. We show that…

Numerical Analysis · Computer Science 2015-06-19 A. Cichocki , D. Mandic , A-H. Phan , C. Caiafa , G. Zhou , Q. Zhao , L. De Lathauwer
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