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Pseudospectral collocation methods have proven to be powerful tools to solve optimal control problems. While these methods generally assume the dynamics is given in the first order form $\dot{x} = f (x, u, t)$, where x is the state and u is…

Robotics · Computer Science 2023-02-20 Siro Moreno-Martín , Lluís Ros , Enric Celaya

When evolving in time the solution of a hyperbolic partial differential equation, it is often desirable to use high order strong stability preserving (SSP) time discretizations. These time discretizations preserve the monotonicity…

Numerical Analysis · Mathematics 2017-08-02 Sidafa Conde , Sigal Gottlieb , Zachary J. Grant , John N. Shadid

Ordinary differential equations of the second order with one constant delay are considered in this paper. An analytical representation of the solution is obtained using the method of steps.

Dynamical Systems · Mathematics 2014-04-29 Oleksandra Kukharenko

Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part that promotes regularization. Most analyses of iteration…

Optimization and Control · Mathematics 2019-01-25 Ching-pei Lee , Stephen J. Wright

Current spectral simulations of Einstein's equations require writing the equations in first-order form, potentially introducing instabilities and inefficiencies. We present a new penalty method for pseudo-spectral evolutions of second order…

General Relativity and Quantum Cosmology · Physics 2014-11-21 Nicholas W. Taylor , Lawrence E. Kidder , Saul A. Teukolsky

This paper studies the problem of stability of a parameterized delay differential equations (DDE see equation (0.1)). After discretizing the DDE (0.1), we show that the problem can be equivalently casted into a semi-definite programming…

Optimization and Control · Mathematics 2017-01-03 Dongcai Su

A new, improved split-step backward Euler (SSBE) method is introduced and analyzed for stochastic differential delay equations(SDDEs) with generic variable delay. The method is proved to be convergent in mean-square sense under conditions…

Numerical Analysis · Mathematics 2011-07-05 Xiaojie Wang , Siqing Gan

The double-exponential Sinc-collocation method is known as a super-accurate method for solving initial value problems of ordinary differential equations, for which the error decreases almost exponentially as a function of the number of…

Numerical Analysis · Mathematics 2026-04-29 Yusaku Yamamoto , Ken'ichiro Tanaka

This paper develops methods for numerically solving stochastic delay-differential equations (SDDEs) with multiple fixed delays that do not align with a uniform time mesh. We focus on numerical schemes of strong convergence orders $1/2$ and…

Numerical Analysis · Mathematics 2026-05-05 Mitchell T. Griggs , Kevin Burrage , Pamela M. Burrage

A spectral decomposition method is used to obtain solutions to a class of nonlinear differential equations. We extend this approach to the analysis of the fractional form of these equations and demonstrate the method by applying it to the…

Mathematical Physics · Physics 2015-08-14 Malgorzata Turalska , Bruce J. West

In this paper, we study the Boltzmann equation with uncertainties and prove that the spectral convergence of the semi-discretized numerical system holds in a combined velocity and random space, where the Fourier-spectral method is applied…

Numerical Analysis · Mathematics 2024-05-08 Liu Liu , Kunlun Qi

Block coordinate descent (BCD) methods approach optimization problems by performing gradient steps along alternating subgroups of coordinates. This is in contrast to full gradient descent, where a gradient step updates all coordinates…

Numerical Analysis · Mathematics 2019-07-29 Simon Rabanser , Lukas Neumann , Markus Haltmeier

A linear implicit finite difference method is proposed for the approximation of the solution to a periodic, initial value problem for a Schrodinger-Hirota equation. Optimal, second order convergence in the discrete $H^1-$norm is proved,…

Numerical Analysis · Mathematics 2017-06-14 Georgios E. Zouraris

(Partial) differential equations (PDEs) are fundamental tools for describing natural phenomena, making their solution crucial in science and engineering. While traditional methods, such as the finite element method, provide reliable…

Machine Learning · Computer Science 2025-03-11 Viggo Moro , Luiz F. O. Chamon

We present a fast iterative solver for scattering problems in 2D, where a penetrable object with compact support is considered. By representing the scattered field as a volume potential in terms of the Green's function, we arrive at the…

Numerical Analysis · Mathematics 2023-03-28 Vaishnavi Gujjula , Sivaram Ambikasaran

Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation. However, generic solvers for SDP are…

Computer Vision and Pattern Recognition · Computer Science 2021-03-17 Álvaro Parra , Shin-Fang Chng , Tat-Jun Chin , Anders Eriksson , Ian Reid

In this work, we investigate a stochastic gradient descent method for solving inverse problems that can be written as systems of linear or nonlinear ill-posed equations in Banach spaces. The method uses only a randomly selected equation at…

Numerical Analysis · Mathematics 2024-09-10 Ruixue Gu , Zhenwu Fu , Bo Han , Hongsun Fu

We investigate two efficient time discretizations for the post-processing technique of discontinuous Galerkin (DG) methods to solve hyperbolic conservation laws. The post-processing technique, which is applied at the final time of the DG…

Numerical Analysis · Mathematics 2023-12-22 Xiaozhou Li

We consider a second order functional-differential pencil with two constant delays of the argument and study the inverse problem of recovering its coefficients from the spectra of two boundary value problems with one common boundary…

Spectral Theory · Mathematics 2020-10-28 S. A. Buterin , M. A. Malyugina , C. -T. Shieh

In this work, we consider the low rank decomposition (SDPR) of general convex semidefinite programming problems (SDP) that contain both a positive semidefinite matrix and a nonnegative vector as variables. We develop a rank-support-adaptive…

Optimization and Control · Mathematics 2023-12-14 Tianyun Tang , Kim-Chuan Toh
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