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Gaussian processes are a versatile probabilistic machine learning model whose effectiveness often depends on good hyperparameters, which are typically learned by maximising the marginal likelihood. In this work, we consider iterative…

Machine Learning · Computer Science 2024-05-29 Jihao Andreas Lin , Shreyas Padhy , Bruno Mlodozeniec , José Miguel Hernández-Lobato

We study the existence, non-existence and multiplicity of prescribed mass positive solutions to a Schr\"odinger equation of the form \begin{equation*} -\Delta u+\lambda u=g(u), \quad u \in H^1(\mathbb{R}^N), \, N \geq 1. \end{equation*} Our…

Analysis of PDEs · Mathematics 2023-11-15 Louis Jeanjean , Jianjun Zhang , Xuexiu Zhong

In the present work, we improve a numerical method, developed to solve the Gross-Pitaevkii nonlinear Schroedinger equation. A particular scaling is used in the equation, which permits to evaluate the wave-function normalization after the…

Soft Condensed Matter · Physics 2009-10-31 A. Gammal , T. Frederico , L. Tomio

Often in applications ranging from medical imaging and sensor networks to error correction and data science (and beyond), one needs to solve large-scale linear systems in which a fraction of the measurements have been corrupted. We consider…

Numerical Analysis · Mathematics 2021-07-09 Jamie Haddock , Deanna Needell , Elizaveta Rebrova , William Swartworth

Recently Ahmadi et al. (2021) and Tagliaferro (2022) proposed some iterative methods for the numerical solution of linear systems which, under the classical hypothesis of strict diagonal dominance, typically converge faster than the Jacobi…

Numerical Analysis · Mathematics 2024-04-11 Paolo Novati , Fulvio Tagliaferro , Marino Zennaro

In this paper, we prove the existence of positive solutions $(\lambda_1,\lambda_2, u,v)\in \R^2\times H^1(\R^N, \R^2)$ to the following coupled Schr\"odinger system $$\begin{cases} -\Delta u + \lambda_1 u= \mu_1|u|^{p-2}u+\beta v \quad…

Analysis of PDEs · Mathematics 2021-08-03 Zhen Chen , Xuexiu Zhong , Wenming Zou

Solving partial differential equations (PDEs) within the framework of probabilistic numerics offers a principled approach to quantifying epistemic uncertainty arising from discretization. By leveraging Gaussian process regression and…

Machine Learning · Statistics 2025-08-18 Akshay Thakur , Sawan Kumar , Matthew Zahr , Souvik Chakraborty

This paper proposes and tests the first-ever reduced basis warm-start iterative method for the parametrized linear systems, exemplified by those discretizing the parametric partial differential equations. Traditional iterative methods are…

Numerical Analysis · Mathematics 2024-01-08 Shijin Hou , Yanlai Chen , Yinhua Xia

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

This document introduces a strategy to solve linear optimization problems. The strategy is based on the bounding condition each constraint produces on each one of the problem's dimension. The solution of a linear optimization problem is…

Optimization and Control · Mathematics 2018-09-24 Gerardo L. Febres

As a classical time-stepping method, it is well-known that the Strang splitting method reaches the first-order accuracy by losing two spatial derivatives. In this paper, we propose a modified splitting method for the 1D cubic nonlinear…

Numerical Analysis · Mathematics 2022-12-20 Yifei Wu

Computing accurate periodic responses in strongly nonlinear or even non-smooth vibration systems remains a fundamental challenge in nonlinear dynamics. Existing numerical methods, such as the Harmonic Balance Method (HBM) and the Shooting…

Numerical Analysis · Mathematics 2025-10-28 Limin Cao , Yanmao Chen , Li Wang , Loic Salles , Zechang Zheng

Recently, a class of algorithms combining classical fixed point iterations with repeated random sparsification of approximate solution vectors has been successfully applied to eigenproblems with matrices as large as $10^{108} \times…

Numerical Analysis · Mathematics 2025-04-28 Jonathan Weare , Robert J. Webber

The matrix Numerov method provides an efficient framework for solving the time-independent Schr\"odinger equation as a matrix eigenvalue problem. However, for singular potentials such as the Coulomb interaction, the expected fourth-order…

Atomic Physics · Physics 2026-03-11 Nir Barnea

In this paper, we investigate the numerical solutions of the cubic nonlinear Schrodinger equation via the exponential B-spline collocation method. Crank-Nicolson formulas are used for time discretization of the target equation. A…

Numerical Analysis · Mathematics 2016-07-04 Ozlem Ersoy , Idris Dag , Ali Sahin

In this work, we propose a generalized alternating Anderson acceleration method, a periodic scheme composed of $t$ fixed-point iteration steps, interleaved with $s$ steps of Anderson acceleration with window size $m$, to solve linear and…

Numerical Analysis · Mathematics 2026-02-02 Yunhui He , Santolo Leveque

Quasi-dynamic energy flow calculation is an indispensable tool for the heat and electricity integrated energy system (HE-IES) analysis. One solves the nonlinear partial differential algebraic equations to obtain thermal, hydraulic and…

Systems and Control · Electrical Eng. & Systems 2022-09-27 Ruizhi Yu , Wei Gu

The object of the present paper is to extend the third-order iterative method for solving nonlinear equations into systems of nonlinear equations. Since our motive is to develop the method which improve the order of convergence of Newton's…

Numerical Analysis · Mathematics 2013-09-24 Anuradha Singh , J. P. Jaiswa

This work addresses techniques to solve convection-diffusion problems based on Hermite interpolation. We extend to the case of these equations a Hermite finite element method providing flux continuity across inter-element boundaries, shown…

Numerical Analysis · Mathematics 2015-12-25 Florin Radu , Vitoriano Ruas , Paulo Trales

Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineering applications, yet existing numerical solvers rely heavily on matrix based discretizations, while learning based methods require…

Machine Learning · Computer Science 2026-04-30 Yi Bing , Zheng Ran , Fu Jinyang , Liu Long , Peng Xiang
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