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This paper develops a new class of linearly implicit time integration schemes called Linearly-Implicit Runge-Kutta-W (LIRK-W) methods. These schemes are based on an implicit-explicit approach which does not require a splitting of the right…

Numerical Analysis · Mathematics 2016-11-22 Paul Tranquilli , Adrian Sandu , Hong Zhang

Segregated Runge-Kutta (SRK) schemes are time integration methods for the incompressible Navier-Stokes equations. In this approach, convection and diffusion can be independently treated either explicitly or implicitly, which in particular…

Numerical Analysis · Mathematics 2025-06-12 Pavel Bakhvalov

This paper investigates the competitiveness of semi-implicit Runge-Kutta (RK) and spectral deferred correction (SDC) time-integration methods up to order six for incompressible Navier-Stokes problems in conjunction with a high-order…

Numerical Analysis · Mathematics 2022-10-03 Montadhar Guesmi , Martina Grotteschi , Jörg Stiller

In this paper, we study a time discrete scheme for the initial value problem of the ES-BGK kinetic equation. Numerically solving these equations are challenging due to the nonlinear stiff collision (source) terms induced by small mean free…

Numerical Analysis · Mathematics 2010-04-01 Francis Filbet , Shi Jin

We present an extrapolation-based semi-implicit multirate time stepping (MRT) scheme and a compound-fast MRT scheme for a naturally partitioned, multi-time-scale hydro-geomechanical hydrate reservoir model. We evaluate the performance of…

Numerical Analysis · Mathematics 2016-04-20 Shubhangi Gupta , Barbara I. Wohlmuth , Rainer Helmig

We present an implicit-explicit well-balanced finite volume scheme for the Euler equations with a gravitational source term which is able to deal also with low Mach flows. To visualize the different scales we use the non-dimensionalized…

Numerical Analysis · Mathematics 2020-08-26 Andrea Thomann , Gabriella Puppo , Christian Klingenberg

We consider a popular family of constrained optimization problems arising in machine learning that involve optimizing a non-decomposable evaluation metric with a certain thresholded form, while constraining another metric of interest.…

Machine Learning · Computer Science 2021-07-30 Abhishek Kumar , Harikrishna Narasimhan , Andrew Cotter

A class of high order asymptotic preserving (AP) schemes has been developed for the BGK equation in Xiong et. al. (2015) [37], which is based on the micro-macro formulation of the equation. The nodal discontinuous Galerkin (NDG) method with…

Numerical Analysis · Mathematics 2016-02-09 Tao Xiong , Jingmei Qiu

By providing a simple and efficient way of computing low-variance gradients of continuous random variables, the reparameterization trick has become the technique of choice for training a variety of latent variable models. However, it is not…

Machine Learning · Computer Science 2019-01-31 Michael Figurnov , Shakir Mohamed , Andriy Mnih

Interval Markov decision processes (IMDPs) generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that…

Systems and Control · Computer Science 2017-07-07 Ernst Moritz Hahn , Vahid Hashemi , Holger Hermanns , Morteza Lahijanian , Andrea Turrini

Quasi-linear hyperbolic systems with source terms introduce significant computational challenges due to the presence of a stiff source term. To address this, a finite volume Nessyahu-Tadmor (NT) central numerical scheme is explored and…

Numerical Analysis · Mathematics 2026-03-30 Sudipta Sahu , Emanuele Macca , Rathan Samala

High-order numerical methods enhance Transformer performance in tasks like NLP and CV, but introduce a performance-efficiency trade-off due to increased computational overhead. Our analysis reveals that conventional efficiency techniques,…

Machine Learning · Computer Science 2025-10-14 Xinyu Liu , Bei Li , Jiahao Liu , Junhao Ruan , Kechen Jiao , Hongyin Tang , Jingang Wang , Xiao Tong , Jingbo Zhu

Inverse kinematics (IK) is central to robot control and motion planning, yet its nonlinear kinematic mapping makes it inherently nonconvex and particularly challenging under complex constraints. We present IKSPARK (Inverse Kinematics using…

Robotics · Computer Science 2026-05-01 Liangting Wu , Roberto Tron

First-order methods with momentum such as Nesterov's fast gradient method are very useful for convex optimization problems, but can exhibit undesirable oscillations yielding slow convergence rates for some applications. An adaptive…

Optimization and Control · Mathematics 2019-06-14 Donghwan Kim , Jeffrey A. Fessler

While Transformers are dominated by Floating-Point (FP) Matrix-Multiplications, their aggressive acceleration through dedicated hardware or many-core programmable systems has shifted the performance bottleneck to non-linear functions like…

Hardware Architecture · Computer Science 2025-04-16 Run Wang , Gamze Islamoglu , Andrea Belano , Viviane Potocnik , Francesco Conti , Angelo Garofalo , Luca Benini

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic…

Systems and Control · Electrical Eng. & Systems 2026-02-18 Giannis Delimpaltadakis , Morteza Lahijanian , Manuel Mazo , Luca Laurenti

A single-step high-order implicit time integration scheme with controllable numerical dissipation at high frequencies is presented for the transient analysis of structural dynamic problems. The amount of numerical dissipation is controlled…

Numerical Analysis · Mathematics 2023-09-29 Chongmin Song , Xiaoran Zhang , Sascha Eisenträger , Ankit Ankit

This paper proposes an implicit family of sub-step integration algorithms grounded in the explicit singly diagonally implicit Runge-Kutta (ESDIRK) method. The proposed methods achieve third-order consistency per sub-step and thus the…

Numerical Analysis · Mathematics 2025-06-05 Jinze Li , Hua Li , Kaiping Yu , Rui Zhao

Electro-quasistatic field problems involving nonlinear materials are commonly discretized in space using finite elements. In this paper, it is proposed to solve the resulting system of ordinary differential equations by an explicit…

Computational Engineering, Finance, and Science · Computer Science 2017-09-26 Christian Richter , Sebastian Schöps , Markus Clemens

Deterministic solutions of the Boltzmann equation represent a real challenge due to the enormous computational effort which is required to produce such simulations and often stochastic methods such as Direct Simulation Monte Carlo (DSMC)…

Numerical Analysis · Mathematics 2021-07-26 Walter Boscheri , Giacomo Dimarco
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