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Related papers: A multistep algorithm for ODEs

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Time integration methods for solving initial value problems are an important component of many scientific and engineering simulations. Implicit time integrators are desirable for their stability properties, significantly relaxing…

Numerical Analysis · Mathematics 2020-11-24 Ross Glandon , Mahesh Narayanamurthi , Adrian Sandu

This study focuses on the numerical discretization methods for the continuous-time discounted linear-quadratic optimal control problem (LQ-OCP) with time delays. By assuming piecewise constant inputs, we formulate the discrete system…

Optimization and Control · Mathematics 2024-07-29 Zhanhao Zhang , Steen Hørsholt , John Bagterp Jørgensen

The purpose of this work is to introduce a new idea of how to avoid the factorization of large matrices during the solution of stiff systems of ODEs. Starting from the general form of an explicit linear multistep method we suggest to…

Numerical Analysis · Mathematics 2019-08-22 Boris Faleichik

In this article a unified approach to iterative soft-thresholding algorithms for the solution of linear operator equations in infinite dimensional Hilbert spaces is presented. We formulate the algorithm in the framework of generalized…

Functional Analysis · Mathematics 2010-10-26 Kristian Bredies , Dirk A. Lorenz

Linear programs (LPs) can be solved by polynomially many moves along the circuit direction improving the objective the most, so-called deepest-descent steps (dd-steps). Computing these steps is NP-hard (De Loera et al., arXiv, 2019), a…

Optimization and Control · Mathematics 2021-01-26 Steffen Borgwardt , Cornelius Brand , Andreas Emil Feldmann , Martin Koutecký

Algorithmic approach to the problem of linearization by point transformation of ordinary differential equation of arbitrary order is presented. Test-linearization is purely algorithmic.

Classical Analysis and ODEs · Mathematics 2017-06-07 Vladimir Gerdt , Dmitry Lyakhov

A three-point iterative method for solving scalar non-linear equations was selected and then adapted to solve systems of non-linear equations. Subsequently, by applying Taylor's theorem to functions of $\R^{n}$ in $\R^{n}$, it is shown that…

General Mathematics · Mathematics 2026-01-23 Carlos E. Cadenas R. , Yorman J. Mendoza N

This work develops a class of probabilistic algorithms for the numerical solution of nonlinear, time-dependent partial differential equations (PDEs). Current state-of-the-art PDE solvers treat the space- and time-dimensions separately,…

Numerical Analysis · Mathematics 2022-03-10 Nicholas Krämer , Jonathan Schmidt , Philipp Hennig

An algorithm is proposed to solve robust control problems constrained by partial differential equations with uncertain coefficients, based on the so-called MG/OPT framework. The levels in this MG/OPT hierarchy correspond to discretization…

Numerical Analysis · Mathematics 2021-07-21 Andreas Van Barel , Stefan Vandewalle

For solving linear ill-posed problems regularization methods are required when the right hand side is with some noise. In the present paper regularized solutions are obtained by implicit iteration methods in Hilbert scales. % By exploiting…

Numerical Analysis · Mathematics 2015-05-20 Qinian Jin , Ulrich Tautenhahn

Stiff systems of ordinary differential equations (ODEs) and sparse training data are common in scientific problems. This paper describes efficient, implicit, vectorized methods for integrating stiff systems of ordinary differential…

Numerical Analysis · Mathematics 2023-10-16 Mark C. Messner , Tianchen Hu , Tianju Chen

In this paper, we deal with the Front Steepest Descent algorithm for multi-objective optimization. We point out that the algorithm from the literature is often incapable, by design, of spanning large portions of the Pareto front. We thus…

Optimization and Control · Mathematics 2023-03-17 Matteo Lapucci , Pierluigi Mansueto

Dahlquist, Liniger, and Nevanlinna design a family of one-leg, two-step methods (the DLN method) that is second order, A- and G-stable for arbitrary, non-uniform time steps. Recently, the implementation of the DLN method can be simplified…

Numerical Analysis · Mathematics 2023-06-06 Wenlong Pei

A higher-order numerical method is presented for scalar valued, coupled forward-backward stochastic differential equations. Unlike most classical references, the forward component is not only discretized by an Euler-Maruyama approximation…

Numerical Analysis · Mathematics 2025-01-22 Balint Negyesi , Cornelis W. Oosterlee

In appropriate frameworks, automatic differentiation is transparent to the user at the cost of being a significant computational burden when the number of operations is large. For iterative algorithms, implicit differentiation alleviates…

Optimization and Control · Mathematics 2023-05-24 Jérôme Bolte , Edouard Pauwels , Samuel Vaiter

Adam is a popular variant of stochastic gradient descent for finding a local minimizer of a function. In the constant stepsize regime, assuming that the objective function is differentiable and non-convex, we establish the convergence in…

Machine Learning · Statistics 2020-05-15 Anas Barakat , Pascal Bianchi

Review of implicit methods of integrating system of stiff ordinary differential equations is presented. Defines and graphically presents absolute stability region for Gears methods (backward differentiation formula) used to solve system of…

Classical Analysis and ODEs · Mathematics 2015-03-17 Mihail Semenov

This paper studies multistep methods for the integration of reversible dynamical systems, with particular emphasis on the planar Kepler problem. It has previously been shown by Cano & Sanz-Serna that reversible linear multisteps for…

Astrophysics · Physics 2009-10-31 Wyn Evans , Scott Tremaine

In this work we present explicit Adams-type multistep methods with extended stability interval, which are analogous to the stabilized Chebyshev Runge--Kutta methods. It is proved that for any $k\geq 1$ there exists an explicit $k$-step…

Numerical Analysis · Mathematics 2020-12-15 Vasily Repnikov , Boris Faleichik , Andrey Moysa

In previous literature, backward error analysis was used to find ordinary differential equations (ODEs) approximating the gradient descent trajectory. It was found that finite step sizes implicitly regularize solutions because terms…

Machine Learning · Computer Science 2024-06-18 Matias D. Cattaneo , Jason M. Klusowski , Boris Shigida
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