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A three-point iterative method for solving scalar non-linear equations was selected and then adapted to solve systems of non-linear equations. Subsequently, by applying Taylor's theorem to functions of $\R^{n}$ in $\R^{n}$, it is shown that…

General Mathematics · Mathematics 2026-01-23 Carlos E. Cadenas R. , Yorman J. Mendoza N

In this work, we propose an efficient and robust multigrid method for solving the time-fractional heat equation. Due to the nonlocal property of fractional differential operators, numerical methods usually generate systems of equations for…

Numerical Analysis · Mathematics 2017-08-28 Francisco J. Gaspar , Carmen Rodrigo

We introduce a method for calculating individual elements of matrix functions. Our technique makes use of a novel series expansion for the action of matrix functions on basis vectors that is memory efficient even for very large matrices. We…

Computational Physics · Physics 2021-11-18 Lev Barash , Stefan Güttel , Itay Hen

This paper designs a distributed least square solution method for a linear algebraic equation over a multiagent network. The coefficient matrix is divided into multiple blocks, and each agent only knows a subset of these blocks. The…

Systems and Control · Electrical Eng. & Systems 2023-12-29 Viet Hoang Pham , Hyo-Sung Ahn

A new method for solving systems of linear algebraic equations of a special type arising in solving problems of image reconstruction has been proposed. This method, due to a certain symmetry of the matrix and the choice of the voxel…

Numerical Analysis · Mathematics 2019-08-30 A. A. Alikhanov , A. M. Apekov , Z. A. Kokov , A. O. Belyaev , L. A. Khamukova

Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task…

Machine Learning · Computer Science 2017-02-20 Yongxin Yang , Timothy Hospedales

Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix completion. Nevertheless, they suffer from two drawbacks:…

Machine Learning · Computer Science 2018-12-05 Duc Minh Nguyen , Evaggelia Tsiligianni , Nikos Deligiannis

This paper focuses on finite-time in-network computation of linear transforms of distributed graph data. Finite-time transform computation problems are of interest in graph-based computing and signal processing applications in which the…

Optimization and Control · Mathematics 2021-04-06 Soummya Kar , Markus Püschel , José M. F. Moura

Solution of the Cox-Thompson inverse scattering problem at fixed energy [1,2,3] is reformulated resulting in semi-analytic equations. The new set of equations for the normalization constants and the nonphysical (shifted) angular momenta are…

Mathematical Physics · Physics 2011-11-28 Tamas Palmai , Miklos Horvath , Barnabas Apagyi

One way of constructing explicit expressions of solutions of integrable systems of Partial Differential Equations (PDEs) goes via the Darboux method. This requires the construction of Darboux matrices. Here we introduce a novel algorithm to…

Exactly Solvable and Integrable Systems · Physics 2014-12-01 Antonio Degasperis

In this work the numerical solution of acoustic tomography problem based on the iterative and functional-analytical algorithms is considered. The mathematical properties of these algorithms were previously described in works of R.G.Novikov…

Numerical Analysis · Mathematics 2024-01-26 A. S. Shurup

For the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi algorithm is presented. Working with regularized systems, the method theoretically reconstructs the true solution by means of the computation of a…

Numerical Analysis · Mathematics 2010-09-29 Claude Brezinski , Paolo Novati , Michela Redivo-Zaglia

A general framework based on Gaussian models and a MAP-EM algorithm is introduced in this paper for solving matrix/table completion problems. The numerical experiments with the standard and challenging movie ratings data show that the…

Machine Learning · Computer Science 2010-10-21 Flavien Léger , Guoshen Yu , Guillermo Sapiro

One of the major challenges of contemporary mathematics is numerical solving of various problems for functional differential equations (FDE), in particular Cauchy problem for delayed and neutral differential equations. Recently large…

Classical Analysis and ODEs · Mathematics 2019-01-09 Josef Rebenda , Zdeněk Šmarda , Yasir Khan

The finite element method is a well-established method for the numerical solution of partial differential equations (PDEs), both linear and nonlinear. However, the repeated reassemblage of finite element matrices for nonlinear PDEs is…

Numerical Analysis · Mathematics 2022-09-12 Yannis Voet

Inspired by the latest developments in multilevel Monte Carlo (MLMC) methods and randomised sketching for linear algebra problems we propose a MLMC estimator for real-time processing of matrix structured random data. Our algorithm is…

Numerical Analysis · Mathematics 2020-04-30 Yue Wu , Nick Polydorides

We report a new analytical method for exact solution of homogeneous linear ordinary differential equations with arbitrary order and variable coefficients. The method is based on the definition of jump transfer matrices and their extension…

Mathematical Physics · Physics 2007-05-23 Sina Khorasani , Ali Adibi

A new semi-analytical solution to the advection-dispersion-reaction equation for modelling solute transport in layered porous media is derived using the Laplace transform. Our solution approach involves introducing unknown functions…

Numerical Analysis · Mathematics 2020-09-29 Elliot J. Carr

We propose and compare new global solution algorithms for continuous time heterogeneous agent economies with aggregate shocks. First, we approximate the agent distribution so that equilibrium in the economy can be characterized by a high,…

Optimization and Control · Mathematics 2024-06-21 Zhouzhou Gu , Mathieu Laurière , Sebastian Merkel , Jonathan Payne

Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Promising methods have been developed to analyze these types of…

Methodology · Statistics 2019-04-30 Arkaprava Roy , Jana Schaich-Borg , David B Dunson