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The eigenvalue problem plays a central role in linear algebra and its applications in control and optimization methods. In particular, many matrix decompositions rely upon computation of eigenvalue-eigenvector pairs, such as diagonal or…

Optimization and Control · Mathematics 2016-07-15 Pavel Osinenko , Grigory Devadze , Stefan Streif

This paper investigates some issues arising in categorical models of reversible logic and computation. Our claim is that the structural (coherence) isomorphisms of these categorical models, although generally overlooked, have decidedly…

Category Theory · Mathematics 2013-04-29 Peter Hines

Aggregation functions are widely used in answer set programming for representing and reasoning on knowledge involving sets of objects collectively. Current implementations simplify the structure of programs in order to optimize the overall…

Artificial Intelligence · Computer Science 2020-02-19 Mario Alviano , Wolfgang Faber , Martin Gebser

This paper is a concise and painless introduction to the $\lambda$-calculus. This formalism was developed by Alonzo Church as a tool for studying the mathematical properties of effectively computable functions. The formalism became popular…

Logic in Computer Science · Computer Science 2015-04-01 Raul Rojas

The nonnegative matrix factorization is a widely used, flexible matrix decomposition, finding applications in biology, image and signal processing and information retrieval, among other areas. Here we present a related matrix factorization.…

Machine Learning · Statistics 2017-12-12 David W Dreisigmeyer

Given a matrix of distribution functions and a quasi-stochastic matrix, i.e. an irreducible nonnegative matrix with maximal eigenvalue one and associated unique positive left and right eigenvectors, the article studies the properties of an…

Probability · Mathematics 2015-08-28 Gerold Alsmeyer

This study presents the derivation of a recursive formula for integrals of products of $N$ Hermite polynomials, establishing a numerically stable scheme for their accurate evaluation in computer codes. The derivation is notably simple and…

Quantum Physics · Physics 2026-02-25 Tran Duong Anh-Tai , Phan Quang Son , Le Minh Khang , Nguyen Duy Vy , Vinh N. T. Pham

The theory of matrix splitting is a useful tool for finding solution of rectangular linear system of equations, iteratively. The purpose of this paper is two-fold. Firstly, we revisit theory of weak regular splittings for rectangular…

Numerical Analysis · Mathematics 2016-08-23 Debasisha Mishra

We investigate the subclass of reversible functions that are self-inverse and relate them to reversible circuits that are equal to their reverse circuit, which are called palindromic circuits. We precisely determine which self-inverse…

Emerging Technologies · Computer Science 2015-02-23 Mathias Soeken , Michael Kirkedal Thomsen , Gerhard W. Dueck , D. Michael Miller

Many real-world applications are addressed through a linear least-squares problem formulation, whose solution is calculated by means of an iterative approach. A huge amount of studies has been carried out in the optimization field to…

Numerical Analysis · Mathematics 2013-11-25 Anastasia Cornelio , Federica Porta , Marco Prato , Luca Zanni

Linear algebra's main concerns are sets of vectors, linear functions, subspaces, linear systems, matrices and concepts about those, such as whether the solution of linear system exists or is unique; a set of vectors is linearly independent…

Symbolic Computation · Computer Science 2025-04-15 Iago Leal de Freitas , Júlia Mota , João Paixão , Lucas Rufino

In this paper we are concerned to find the eigenvalues and eigenvectors of a real symetric matrix by applying a new numerical method similar to Jacobi method. Our approch consists to use a new orthogonal matrix. The computation of the…

Numerical Analysis · Mathematics 2020-03-30 Nassim Guerraiche

A matrix network is a family of matrices, with relatedness modeled by a weighted graph. We consider the task of completing a partially observed matrix network. We assume a novel sampling scheme where a fraction of matrices might be…

Machine Learning · Computer Science 2018-06-11 Qingyun Sun , Mengyuan Yan David Donoho , Stephen Boyd

The Eigendecomposition of quadratic forms (symmetric matrices) guaranteed by the spectral theorem is a foundational result in applied mathematics. Motivated by a shared structure found in inferential problems of recent interest---namely…

Machine Learning · Computer Science 2018-02-26 Mikhail Belkin , Luis Rademacher , James Voss

Using three different approaches, we analyze the complexity of various birational maps constructed from simple operations (inversions) on square matrices of arbitrary size. The first approach consists in the study of the images of lines,…

Mathematical Physics · Physics 2011-11-10 Jean Christian Angles D'Auriac , Jean-Marie Maillard , Claude Viallet

The exponential of the triangular matrix whose entries in the diagonal at distance $n$ from the principal diagonal are all equal to the sum of the inverse of the divisors of $n$ is the triangular matrix whose entries in the diagonal at…

Number Theory · Mathematics 2008-06-10 Aicardi Francesca

Diagonalization, or eigenvalue decomposition, is very useful in many areas of applied mathematics, including signal processing and quantum physics. Matrix decomposition is also a useful tool for approximating matrices as the product of a…

Spectral Theory · Mathematics 2016-06-07 Théo Trouillon , Christopher R. Dance , Éric Gaussier , Guillaume Bouchard

A recursive calculational scheme is developed for matrix elements in the generalized seniority scheme for the nuclear shell model. Recurrence relations are derived which permit straightforward and efficient computation of matrix elements of…

Nuclear Theory · Physics 2015-03-18 F. Q. Luo , M. A. Caprio

In exploratory factor analysis, rotation techniques are employed to derive interpretable factor loading matrices. Factor rotations deal with equality-constrained optimization problems aimed at determining a loading matrix based on measure…

Statistics Theory · Mathematics 2025-05-01 Ryoya Fukasaku , Michio Yamamoto , Yutaro Kabata , Yasuhiko Ikematsu , Kei Hirose

We present new algorithms that efficiently approximate the hypergeometric function of a matrix argument through its expansion as a series of Jack functions. Our algorithms exploit the combinatorial properties of the Jack function, and have…

Probability · Mathematics 2007-05-23 Plamen Koev , Alan Edelman