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This is one of our series papers on multistep schemes for solving forward backward stochastic differential equations (FBSDEs) and related problems. Here we extend (with non-trivial updates) our multistep schemes in [W. Zhao, Y. Fu and T.…

Numerical Analysis · Mathematics 2015-02-12 Kong Tao , Weidong Zhao , Tao Zhou

This paper is concerned with the construction and convergence analysis of novel implicit Peer triplets of two-step nature with four stages for nonlinear ODE constrained optimal control problems. We combine the property of superconvergence…

Optimization and Control · Mathematics 2022-06-14 Jens Lang , Bernhard A. Schmitt

An explicit stabilized additive Runge-Kutta scheme is proposed. The method is based on a splitting of the problem in severely stiff and mildly stiff subproblems, which are then independently solved using a Runge-Kutta-Chebyshev scheme. The…

Numerical Analysis · Mathematics 2020-03-09 Assyr Abdulle , Giacomo Rosilho de Souza

In this paper, authors successfully construct a new algorithm for the new higher order scheme of weak approximation of SDEs. The algorithm presented here is based on [1][2]. Although this algorithm shares some features with the algorithm…

Probability · Mathematics 2025-04-28 Mariko Ninomiya , Syoiti Ninomiya

In this paper, we study the fundamental open question of finding the optimal high-order algorithm for solving smooth convex minimization problems. Arjevani et al. (2019) established the lower bound $\Omega\left(\epsilon^{-2/(3p+1)}\right)$…

Optimization and Control · Mathematics 2022-05-20 Dmitry Kovalev , Alexander Gasnikov

Accurate and efficient simulation of fluid-structure interaction (FSI) problems remains a central challenge in computational physics. High-order discontinuous Galerkin (DG) methods offer low numerical errors and excellent scalability on…

Fluid Dynamics · Physics 2025-12-08 Yingjie Xia , Stefano Colombo , David Huergo , Jiaqing Kou , Yuting Dai , Esteban Ferrer

We leverage second-order information for tuning of inverse optimal controllers for a class of discrete-time nonlinear input-affine systems. For this, we select the input penalty matrix, representing a tuning knob, to yield the Hessian of…

Optimization and Control · Mathematics 2022-11-17 Taouba Jouini , Zhiyong Sun , Venkatraman Renganathan

Multirate time integration methods apply different step sizes to resolve different components of the system based on the local activity levels. This local selection of step sizes allows increased computational efficiency while achieving the…

Numerical Analysis · Computer Science 2021-12-22 Arash Sarshar , Steven Roberts , Adrian Sandu

This work gives the asymptotic error distribution of the stochastic Runge--Kutta (SRK) method of strong order $1$ applied to Stratonovich-type stochastic differential equations. For dealing with the implicitness introduced in the diffusion…

Numerical Analysis · Mathematics 2025-08-05 Diancong Jin

The high-order numerical solution of the non-linear shallow water equations (and of hyperbolic systems in general) is susceptible to unphysical Gibbs oscillations that form in the proximity of strong gradients. The solution to this problem…

Numerical Analysis · Mathematics 2016-07-18 Simone Marras , Michal A. Kopera , Emil M. Constantinescu , Jenny Suckale , Francis X. Giraldo

This study focuses on the development and analysis of a group of high-order implicit-explicit (IMEX) Runge--Kutta (RK) methods that are suitable for discretizing gradient flows with nonlinearity that is Lipschitz continuous. We demonstrate…

Numerical Analysis · Mathematics 2024-03-22 Zhaohui Fu , Tao Tang , Jiang Yang

Explicit Runge-Kutta schemes with large stable step sizes are developed for integration of high order spectral difference spatial discretization on quadrilateral grids. The new schemes permit an effective time step that is substantially…

Numerical Analysis · Mathematics 2013-07-16 M. Parsani , D. I. Ketcheson , W. Deconinck

In this paper, we propose a class of super-schemes for efficiently solving nonlinear unconstrained optimization problems. The proposed approach introduces two novel choices of step-size parameters, leading to efficient descent directions…

Optimization and Control · Mathematics 2026-04-24 Tugal Zhanlav , Lkhamsuren Altangerel , Khuder Otgondorj

The solution of time-dependent hyperbolic conservation laws on cut cell meshes causes the small cell problem: standard schemes are not stable on the arbitrarily small cut cells if an explicit time stepping scheme is used and the time step…

Numerical Analysis · Mathematics 2023-10-26 Sandra May , Fabian Laakmann

This work considers multirate generalized-structure additively partitioned Runge-Kutta (MrGARK) methods for solving stiff systems of ordinary differential equations (ODEs) with multiple time scales. These methods treat different partitions…

Numerical Analysis · Mathematics 2022-01-19 Steven Roberts , John Loffeld , Arash Sarshar , Carol S. Woodward , Adrian Sandu

This paper develops the high-order accurate entropy stable finite difference schemes for one- and two-dimensional special relativistic hydrodynamic equations. The schemes are built on the entropy conservative flux and the weighted…

Numerical Analysis · Mathematics 2020-03-30 Junming Duan , Huazhong Tang

We consider the efficient numerical solution of coupled dynamical systems, consisting of a small nonlinear part and a large linear time invariant part, possibly stemming from spatial discretization of an underlying partial differential…

Numerical Analysis · Mathematics 2018-11-27 Herbert Egger , Vsevolod Shashkov , Kersten Schmidt

We introduce a high-order space-time approximation of the Shallow Water Equations with sources that is invariant-domain preserving (IDP) and well-balanced with respect to rest states. The employed time-stepping technique is a novel explicit…

Numerical Analysis · Mathematics 2025-09-09 Jean-Luc Guermond , Matthias Maier , Eric Tovar

In this paper, we compare the impact of iterated and direct approaches to sensitivity computation in fixed-step explicit singly diagonally-implicit Runge-Kutta (ESDIRK) methods when applied to optimal control problems (OCPs). We use the…

Systems and Control · Electrical Eng. & Systems 2024-02-08 Anders Hilmar Damm Andersen , John Bagterp Jørgensen

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second…

Machine Learning · Computer Science 2021-03-08 Rohan Anil , Vineet Gupta , Tomer Koren , Kevin Regan , Yoram Singer
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