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This work focuses on the development of a new class of high-order accurate methods for multirate time integration of systems of ordinary differential equations. Unlike other recent work in this area, the proposed methods support mixed…

Numerical Analysis · Mathematics 2023-01-04 Rujeko Chinomona , Daniel R. Reynolds

We consider the construction of semi-implicit linear multistep methods which can be applied to time dependent PDEs where the separation of scales in additive form, typically used in implicit-explicit (IMEX) methods, is not possible. As…

Numerical Analysis · Mathematics 2020-01-14 Giacomo Albi , Lorenzo Pareschi

This work is concerned with the uniform accuracy of implicit-explicit backward differentiation formulas for general linear hyperbolic relaxation systems satisfying the structural stability condition proposed previously by the third author.…

Numerical Analysis · Mathematics 2023-10-10 Zhiting Ma , Juntao Huang , Wen-An Yong

Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees…

Optimization and Control · Mathematics 2024-07-01 Xufeng Cai , Jelena Diakonikolas

We present an implicit-explicit (IMEX) scheme for semilinear wave equations with strong damping. By treating the nonlinear, nonstiff term explicitly and the linear, stiff part implicitly, we obtain a method which is not only unconditionally…

Numerical Analysis · Mathematics 2024-07-01 Daniel Eckhardt , Marlis Hochbruck , Barbara Verfürth

Implicit-Explicit (IMEX) schemes are widely used for time integration methods for approximating solutions to a large class of problems. In this work, we develop accurate a posteriori error estimates of a quantity of interest for…

Numerical Analysis · Mathematics 2016-10-19 Jehanzeb H. Chaudhry , J. B. Collins , John N. Shadid

Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep networks while using significantly less memory and computational…

Machine Learning · Computer Science 2022-05-17 Tianxiang Gao , Hongyang Gao

Based on a result by Taylor, Hendrickx, and Glineur (J. Optim. Theory Appl., 178(2):455--476, 2018) on the attainable convergence rate of gradient descent for smooth and strongly convex functions in terms of function values, an elementary…

Optimization and Control · Mathematics 2022-03-25 André Uschmajew , Bart Vandereycken

We propose a calculus for modeling implicit programming that supports first-class, overlapping, locally scoped, and higher-order instances with higher-kinded types. We propose a straightforward generalization of the well-established System…

Programming Languages · Computer Science 2025-04-01 Eugène Flesselle

This paper is concerned about the implicit-explicit (IMEX) methods for a class of dissipative wave systems with time-varying velocity feedbacks and nonlinear potential energies, equipped with different boundary conditions. Firstly, we…

Numerical Analysis · Mathematics 2024-10-29 Zhe Jiao , Yaxu Li , Lijing Zhao

We consider solving nonconvex composite optimization problems in which the sum of a smooth function and a nonsmooth function is minimized. Many of convergence analyses of proximal gradient-type methods rely on global descent property…

Optimization and Control · Mathematics 2026-04-09 Shotaro Yagishita , Masaru Ito

We study the rate of convergence of an explicit and an implicit-explicit finite difference scheme for linear stochastic integro-differential equations of parabolic type arising in non-linear filtering of jump-diffusion processes. We show…

Probability · Mathematics 2016-09-09 Konstantinos Dareiotis , James-Michael Leahy

The constrained gradient method (CGM) has recently been proposed to solve convex optimization and monotone variational inequality (VI) problems with general functional constraints. While existing literature has established convergence…

Optimization and Control · Mathematics 2025-11-24 Danqing Zhou , Hongmei Chen , Shiqian Ma , Junfeng Yang

In this work we are interested in general linear inverse problems where the corresponding forward problem is solved iteratively using fixed point methods. Then one-shot methods, which iterate at the same time on the forward problem solution…

Numerical Analysis · Mathematics 2024-05-15 Marcella Bonazzoli , Houssem Haddar , Tuan Anh Vu

The standard assumption for proving linear convergence of first order methods for smooth convex optimization is the strong convexity of the objective function, an assumption which does not hold for many practical applications. In this…

Optimization and Control · Mathematics 2016-08-10 I. Necoara , Yu. Nesterov , F. Glineur

We construct new higher-order implicit-explicit (IMEX) schemes using the generalized scalar auxiliary variable (GSAV) approach for the Landau-Lifshitz equation. These schemes are linear, length preserving and only require solving one…

Numerical Analysis · Mathematics 2024-04-16 Xiaoli Li , Nan Zheng , Jie Shen

Implicit methods for the numerical solution of initial-value problems may admit multiple solutions at any given time step. Accordingly, their nonlinear solvers may converge to any of these solutions. Below a critical timestep, exactly one…

Numerical Analysis · Mathematics 2019-12-20 K. R. Green , G. W. Patrick , R. J. Spiteri

A simple iterative approach for solving a set of implicit kinetic moment equations is proposed. This implicit solve is a key component in the IMEX discretization of the multi-species Bhatnagar-Gross-Krook (M-BGK) model with nontrivial…

Numerical Analysis · Mathematics 2024-04-30 Evan Habbershaw , Cory D. Hauck , Steven M. Wise

The main goal of this paper is to investigate the order reduction phenomenon that appears in the integral deferred correction (InDC) methods based on implicit-explicit (IMEX) Runge-Kutta (R-K) schemes when applied to a class of stiff…

Numerical Analysis · Mathematics 2017-01-18 S. Boscarino , J. Qiu , G. Russo

A generalized conditional gradient method for minimizing the sum of two convex functions, one of them differentiable, is presented. This iterative method relies on two main ingredients: First, the minimization of a partially linearized…

Optimization and Control · Mathematics 2021-10-01 Karl Kunisch , Daniel Walter