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This paper presents a deep learning based model predictive control algorithm for control affine nonlinear discrete time systems with matched and bounded state dependent uncertainties of unknown structure. Since the structure of…

Optimization and Control · Mathematics 2021-09-28 Prabhat K. Mishra , Mateus V. Gasparino , Andres E. B. Velsasquez , Girish Chowdhary

Semi-implicit multilevel spectral deferred correction (SI-MLSDC) methods provide a promising approach for high-order time integration for nonlinear evolution equations including conservation laws. However, existing methods lack robustness…

Numerical Analysis · Mathematics 2025-12-09 Erik Pfister , Jörg Stiller

In this paper the numerical approximation of stochastic differential equations satisfying a global monotonicity condition is studied. The strong rate of convergence with respect to the mean square norm is determined to be $\frac{1}{2}$ for…

Numerical Analysis · Mathematics 2017-09-01 Adam Andersson , Raphael Kruse

In this paper we establish a stability barrier of a class of high-order Hermite-type discretization of 1D advection equations underlying the hybrid-variable (HV) and active flux (AF) methods. These methods seek numerical approximations to…

Numerical Analysis · Mathematics 2025-05-12 Xianyi Zeng

A recent paper [J. A. Evans, D. Kamensky, Y. Bazilevs, "Variational multiscale modeling with discretely divergence-free subscales", Computers & Mathematics with Applications, 80 (2020) 2517-2537] introduced a novel stabilized finite element…

Numerical Analysis · Mathematics 2021-12-21 Sajje Lee Calfy , John A. Evans , David Kamensky

In this paper, based on a generalized scalar auxiliary variable approach with relaxation (R-GSAV), we construct a class of high-order backward differentiation formula (BDF) schemes with variable time steps for the…

Numerical Analysis · Mathematics 2025-06-10 Dawei Chen , Qinzhen Ren , Minghui Li

A second-order accurate modular algorithm is presented for a standard BDF2 code for the Navier-Stokes equations (NSE). The algorithm exhibits resistance to solver breakdown and increased computational efficiency for increasing values of…

Numerical Analysis · Mathematics 2018-06-29 Y. Rong , J. A. Fiordilino

Error bounds for fully discrete schemes for the evolutionary incompressible Navier--Stokes equations are derived in this paper. For the time integration we apply BDF-$q$ methods, $q\le 5$, for which error bounds for $q\ge 3$ cannot be found…

Numerical Analysis · Mathematics 2025-06-23 Bosco García-Archilla , V. John , Julia Novo

The spectral deferred correction (SDC) method is an iterative scheme for computing a higher-order collocation solution to an ODE by performing a series of correction sweeps using a low-order timestepping method. This paper examines a…

Numerical Analysis · Mathematics 2015-10-09 Robert Speck , Daniel Ruprecht , Matthew Emmett , Michael Minion , Matthias Bolten , Rolf Krause

This work studies the design problem of feedback stabilizers for discrete-time systems with input delays. A backstepping procedure is proposed for disturbance-free discrete-time systems. The feedback law designed by using backstepping…

Optimization and Control · Mathematics 2012-12-05 Iasson Karafyllis , Miroslav Krstic

Temporal difference (TD) learning is a foundational algorithm in reinforcement learning (RL). For nearly forty years, TD learning has served as a workhorse for applied RL as well as a building block for more complex and specialized…

Machine Learning · Computer Science 2025-06-24 Hwanwoo Kim , Panos Toulis , Eric Laber

The Poisson-Nernst-Planck equations with generalized Frumkin-Butler-Volmer boundary conditions (PNP-FBV) describe ion transport with Faradaic reactions and have applications in a wide variety of fields. Using an adaptive time-stepper based…

Numerical Analysis · Mathematics 2020-06-24 M. C. Pugh , D. Yan , F. P. Dawson

We analyze (stochastic) gradient descent (SGD) with delayed updates on smooth quasi-convex and non-convex functions and derive concise, non-asymptotic, convergence rates. We show that the rate of convergence in all cases consists of two…

Machine Learning · Computer Science 2021-06-17 Sebastian U. Stich , Sai Praneeth Karimireddy

In this work we present an a priori error analysis for solving the unsteady advection equation on cut cell meshes along a straight ramp in two dimensions. The space discretization uses a lowest order upwind-type discontinuous Galerkin…

Numerical Analysis · Mathematics 2024-11-18 Gunnar Birke , Christian Engwer , Jan Giesselmann , Sandra May

We show that accelerated gradient descent, averaged gradient descent and the heavy-ball method for non-strongly-convex problems may be reformulated as constant parameter second-order difference equation algorithms, where stability of the…

Machine Learning · Statistics 2015-04-08 Nicolas Flammarion , Francis Bach

The Deferred Correction (DeC) is an iterative procedure, characterized by increasing accuracy at each iteration, which can be used to design numerical methods for systems of ODEs. The main advantage of such framework is the automatic way of…

Numerical Analysis · Mathematics 2023-11-09 Lorenzo Micalizzi , Davide Torlo

In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm…

Optimization and Control · Mathematics 2015-05-11 Kimon Fountoulakis , Rachael Tappenden

This paper investigates the long time stability behavior of multiphysics flow problems, namely the Navier-Stokes equations, natural convection and double-diffusive convection equations with an extrapolated blended BDF time-stepping scheme.…

Numerical Analysis · Mathematics 2018-10-25 Aytekin Çıbık , Fatma G. Eroglu , Songul Kaya

Controlling the false discovery rate (FDR) is a powerful approach to multiple testing. In many applications, the tested hypotheses have an inherent hierarchical structure. In this paper, we focus on the fixed sequence structure where the…

Methodology · Statistics 2016-11-11 Gavin Lynch , Wenge Guo , Sanat K. Sarkar , Helmut Finner

To predict allowable time-step size for the fully discretized nonlinear differential equations, a stability theory is developed using exact determination of an infinite perturbation series. Mathematical induction is used to determine the…

Numerical Analysis · Mathematics 2013-11-05 Arash Ghasemi , Kidambi Sreenivas , Lafayette K. Taylor
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