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Locally refined meshes impose severe stability constraints on explicit time-stepping methods for the numerical simulation of time dependent wave phenomena. Local time-stepping methods overcome that bottleneck by using smaller time-steps…

Numerical Analysis · Mathematics 2012-10-19 Marcus Grote , Teodora Mitkova

We present a family of multistep integrators based on the Adams-Bashforth methods. These schemes can be constructed for arbitrary convergence order with arbitrary step size variation. The step size can differ between different subdomains of…

Numerical Analysis · Mathematics 2020-06-19 William Throwe , Saul A. Teukolsky

For the simulations of unsteady flow, the global time step becomes really small with a large variation of local cell size. In this paper, an implicit high-order gas-kinetic scheme (HGKS) is developed to remove the restrictions on the time…

Numerical Analysis · Mathematics 2024-03-04 Yaqing Yang , Liang Pan , Kun Xu

A general conversion strategy by involving a shifted parameter $\theta$ is proposed to construct high-order accuracy difference formulas for fractional calculus operators. By converting the second-order backward difference formula with such…

Numerical Analysis · Mathematics 2022-04-13 Baoli Yin , Guoyu Zhang , Yang Liu , Hong Li

When integrating the radiative transfer equation for polarized light, the necessity of high-order numerical methods is well known. In fact, well-performing high-order formal solvers enable higher accuracy and the use of coarser spatial…

Solar and Stellar Astrophysics · Physics 2017-09-06 Gioele Janett , Oskar Steiner , Luca Belluzzi

For high order (than two) in time operator-splitting methods applied to dissipative systems, a folklore issue is the appearance of negative-time/backward-in-time linear evolution operators such as backward heat operators interwoven with…

Numerical Analysis · Mathematics 2021-07-16 Dong Li , Chaoyu Quan

Weighted finite-state machines are a fundamental building block of NLP systems. They have withstood the test of time -- from their early use in noisy channel models in the 1990s up to modern-day neurally parameterized conditional random…

Computation and Language · Computer Science 2023-09-29 Ran Zmigrod , Tim Vieira , Ryan Cotterell

Decentralized optimization to minimize a finite sum of functions over a network of nodes has been a significant focus within control and signal processing research due to its natural relevance to optimal control and signal estimation…

Machine Learning · Computer Science 2020-09-15 Ran Xin , Shi Pu , Angelia Nedić , Usman A. Khan

This paper proposes a frequency/time hybrid integral-equation method for the time dependent wave equation in two and three-dimensional spatial domains. Relying on Fourier Transformation in time, the method utilizes a fixed…

Numerical Analysis · Mathematics 2020-04-30 Thomas G. Anderson , Oscar P. Bruno , Mark Lyon

Fractional order controllers become increasingly popular due to their versatility and superiority in various performance. However, the bottleneck in deploying these tools in practice is related to their analog or numerical implementation.…

Numerical Analysis · Mathematics 2021-01-28 Yiheng Wei , YangQuan Chen , Yingdong Wei , Xuefeng Zhang

We reconsider the variational integration of optimal control problems for mechanical systems based on a direct discretization of the Lagrange-d'Alembert principle. This approach yields discrete dynamical constraints which by construction…

Optimization and Control · Mathematics 2012-04-30 C. M. Campos , O. Junge , S. Ober-Blöbaum

In this work, we present a second-order nonuniform time-stepping scheme for the time-fractional Allen-Cahn equation. We show that the proposed scheme preserves the discrete maximum principle, and by using the convolution structure of…

Numerical Analysis · Mathematics 2022-01-05 Hong-lin Liao , Tao Tang , Tao Zhou

We construct new, efficient, and accurate high-order finite differencing operators which satisfy summation by parts. Since these operators are not uniquely defined, we consider several optimization criteria: minimizing the bandwidth, the…

General Relativity and Quantum Cosmology · Physics 2008-11-26 Peter Diener , Ernst Nils Dorband , Erik Schnetter , Manuel Tiglio

Many low-Mach or all-Mach number codes are based on space discretizations which in combination with the first order explicit Euler method as time integration would lead to an unstable scheme. In this paper, we investigate how the choice of…

Numerical Analysis · Mathematics 2023-09-14 Friedemann Kemm

We propose a high-order numerical methodology for computing the ground state and time evolution of the two-dimensional Gross-Pitaevskii equation with harmonic trapping potential. The ground state is obtained by combining normalized gradient…

Numerical Analysis · Mathematics 2026-05-29 Roberto Ben , Agustín Besteiro , Diego Rial

We present high-order variational Lagrangian finite element methods for compressible fluids using a discrete energetic variational approach. Our spatial discretization is mass/momentum/energy conserving and entropy stable. Fully implicit…

Numerical Analysis · Mathematics 2023-08-16 Guosheng Fu , Chun Liu

In this paper, we study the affine phase retrieval problem, which aims to recover signals from the magnitudes of affine measurements. We develop second-order optimization methods based on Newton and Gauss-Newton iterations and establish…

Information Theory · Computer Science 2025-04-03 Bing Gao

In this paper, we propose, analyze, and test a new fully discrete, efficient, decoupled, stable, and practically second-order time-stepping algorithm for computing MHD ensemble flow averages under uncertainties in the initial conditions and…

Numerical Analysis · Mathematics 2021-01-19 Muhammad Mohebujjaman

This study presents a sampling-based method to guarantee robust stability of general control systems with uncertainty. The method allows the system dynamics and controllers to be represented by various data-driven models, such as Gaussian…

Optimization and Control · Mathematics 2025-04-11 Yuji Ito , Kenji Fujimoto

Approximate second-order optimization methods often exhibit poorer generalization compared to first-order approaches. In this work, we look into this issue through the lens of the loss landscape and find that existing second-order methods…

Machine Learning · Computer Science 2025-06-25 Dahun Shin , Dongyeop Lee , Jinseok Chung , Namhoon Lee
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