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Understanding how time delays impact the stability of a delay differential equation is important for modeling many natural and technological systems that experience time delays. Here we introduce a new stability criterion for…

Dynamical Systems · Mathematics 2025-08-25 Quinlan Leishman , Benjamin Webb

The selective frequency damping (SFD) method is an alternative to classical Newton's method to obtain unstable steady-state solutions of dynamical systems. However this method has two main limitations: it does not converge for arbitrary…

Fluid Dynamics · Physics 2015-10-28 Bastien E. Jordi , Colin J. Cotter , Spencer J. Sherwin

A nonlinear partial differential equation is a nonlinear relationship between an unknown function and how it changes due to two or more input variables. A numerical method reduces such an equation to arithmetic for quick visualization, but…

History and Overview · Mathematics 2019-09-27 R. Corban Harwood

Ensuring the long-term reproducibility of data analyses requires results stability tests to verify that analysis results remain within acceptable variation bounds despite inevitable software updates and hardware evolutions. This paper…

This paper concerns the stability of analytical and numerical solutions of nonlinear stochastic delay differential equations (SDDEs). We derive sufficient conditions for the stability, contractivity and asymptotic contractivity in mean…

Numerical Analysis · Mathematics 2014-01-21 Siqing Gan , Aiguo Xiao , Desheng Wang

The stability method is very useful for obtaining exact solutions of many extremal graph problems. Its key step is to establish the stability property which, roughly speaking, states that any two almost optimal graphs of the same order $n$…

Combinatorics · Mathematics 2010-07-30 Oleg Pikhurko

In this paper stability and error estimates for time discretizations of linear and semilinear parabolic equations by the two-step backward differentiation formula (BDF2) method with variable step-sizes are derived. An affirmative answer is…

Numerical Analysis · Mathematics 2020-03-10 Wansheng Wang , Mengli Mao , Zheng Wang

Sparse signal recovery based on nonconvex and nonsmooth optimization problems has significant applications and demonstrates superior performance in signal processing and machine learning. This work deals with a scale-invariant…

Optimization and Control · Mathematics 2025-09-29 Lang Yu , Nanjing Huang

Computational methods for fractional differential equations exhibit essential instability. Even a minor modification of the coefficients or other entry data may switch good results to the divergent. The goal of this paper is to suggest the…

Numerical Analysis · Mathematics 2021-12-20 P. B. Dubovski , J. A. Slepoi

This paper introduces a novel method for numerically stabilizing sequential continuous adjoint flow solvers utilizing an elliptic relaxation strategy. The proposed approach is formulated as a Partial Differential Equation (PDE) containing a…

Fluid Dynamics · Physics 2025-01-23 Niklas Kühl

In this study, a stabilized finite element analysis of unified Stokes-Darcy-Brinkman system fully coupled with variable coefficient Advection-Diffusion-Reaction equation(VADR) has been carried out. The viscosity of the fluid, involved in…

Analysis of PDEs · Mathematics 2019-11-27 B. V. Rathish Kumar , Manisha Chowdhury

For complex nonlinear systems, it is challenging to design algorithms that are fast, scalable, and give an accurate approximation of the stability region. This paper proposes a sampling-based approach to address these challenges. By…

Systems and Control · Electrical Eng. & Systems 2024-05-24 Péter Antal , Tamás Péni , Roland Tóth

We present a computationally efficient algorithm for stable numerical differentiation from noisy, uniformly-sampled data on a bounded interval. The method combines multi-interval Fourier extension approximations with an adaptive domain…

Numerical Analysis · Mathematics 2025-08-29 Zhenyu Zhao , Yanfei Wang , Xinran Liu

This paper considers large-scale linear stochastic systems representing, e.g., spatially discretized stochastic partial differential equations. Since asymptotic stability can often not be ensured in such a stochastic setting (e.g. due to…

Numerical Analysis · Mathematics 2022-05-19 Martin Redmann , Nahid Jamshidi

The Adomian decomposition method (ADM) is a universal approach to solving governing equations in various engineering and technological applications. The applicability of the ADM is almost limitless due to its universal applicability, but…

Computational Physics · Physics 2025-01-22 Albert S. Kim

We carry out a comprehensive linear stability analysis of active Brownian particle systems around a constant homogeneous state. These scalar models, being important prototypes for the continuous description of active matter, are…

Analysis of PDEs · Mathematics 2025-12-22 Michele Coti Zelati , Lucas Ertzbischoff , David Gerard-Varet

This paper proposes and analyzes a dampened proximal alternating direction method of multipliers (DP.ADMM) for solving linearly-constrained nonconvex optimization problems where the smooth part of the objective function is nonseparable.…

Optimization and Control · Mathematics 2023-01-05 Weiwei Kong , Renato D. C. Monteiro

In this paper, we study the polynomial stability of analytical solution and convergence of the semi-implicit Euler method for non-linear stochastic pantograph differential equations. Firstly, the sufficient conditions for solutions to grow…

Numerical Analysis · Mathematics 2015-02-03 M. H. Song , Y. L. Lu , M. Z. Liu

Structure-preserving numerical schemes for a nonlinear parabolic fourth-order equation, modeling the electron transport in quantum semiconductors, with periodic boundary conditions are analyzed. First, a two-step backward differentiation…

Numerical Analysis · Mathematics 2012-08-28 Mario Bukal , Etienne Emmrich , Ansgar Jüngel

We develop theory for nonlinear dimensionality reduction (NLDR). A number of NLDR methods have been developed, but there is limited understanding of how these methods work and the relationships between them. There is limited basis for using…

Machine Learning · Statistics 2018-03-08 Daniel Ting , Michael I. Jordan