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In this paper a family of fixed point algorithms for the numerical resolution of some systems of nonlinear equations is designed and analyzed. The family introduced here generalizes the Petviashvili method and can be applied to the…

Numerical Analysis · Mathematics 2013-11-12 J. Alvarez , A. Duran

A family of fixed-point iterations is proposed for the numerical computation of traveling waves and localized ground states. The methods are extended versions of Petviashvili type, and they are applicable when the nonlinear term of the…

Numerical Analysis · Mathematics 2015-06-17 J. Alvarez , A. Duran

A pervasive approach in scientific computing is to express the solution to a given problem as the limit of a sequence of vectors or other mathematical objects. In many situations these sequences are generated by slowly converging iterative…

Numerical Analysis · Mathematics 2025-07-17 Yousef Saad

Proposed in this paper is a numerical procedure to generate periodic traveling wave solutions of some nonlinear dispersive wave equations. The method is based on a suitable modification of a fixed point algorithm of Petviahvili type and…

Numerical Analysis · Mathematics 2015-05-22 J. Alvarez , A. Duran

We consider the application of the type-I Anderson acceleration to solving general non-smooth fixed-point problems. By interleaving with safe-guarding steps, and employing a Powell-type regularization and a re-start checking for strong…

Optimization and Control · Mathematics 2018-08-14 Junzi Zhang , Brendan O'Donoghue , Stephen Boyd

Two adaptive relaxation strategies are proposed for Anderson acceleration. They are specifically designed for applications in which mappings converge to a fixed point. Their superiority over alternative Anderson acceleration is demonstrated…

Numerical Analysis · Mathematics 2024-09-02 Nicolas Lepage-Saucier

Two accelerated imaginary-time evolution methods are proposed for the computation of solitary waves in arbitrary spatial dimensions. For the first method (with traditional power normalization), the convergence conditions as well as…

Pattern Formation and Solitons · Physics 2007-11-22 Jianke Yang , Taras I. Lakoba

In this work, we propose a generalized alternating Anderson acceleration method, a periodic scheme composed of $t$ fixed-point iteration steps, interleaved with $s$ steps of Anderson acceleration with window size $m$, to solve linear and…

Numerical Analysis · Mathematics 2026-02-02 Yunhui He , Santolo Leveque

This monograph covers some recent advances in a range of acceleration techniques frequently used in convex optimization. We first use quadratic optimization problems to introduce two key families of methods, namely momentum and nested…

Optimization and Control · Mathematics 2024-09-26 Alexandre d'Aspremont , Damien Scieur , Adrien Taylor

The alternating direction method of multipliers (ADMM) is a popular approach for solving optimization problems that are potentially non-smooth and with hard constraints. It has been applied to various computer graphics applications,…

Graphics · Computer Science 2019-09-04 Juyong Zhang , Yue Peng , Wenqing Ouyang , Bailin Deng

Travelling wave in a helical wave guide is considered for electron acceleration. A first determination of the travelling wave modes using a partial wave expansion (PWE) and a point matching method is presented. It gives a rapid solution for…

Accelerator Physics · Physics 2011-02-23 X. Artru , C. Ray

This paper examines a number of extrapolation and acceleration methods, and introduces a few modifications of the standard Shanks transformation that deal with general sequences. One of the goals of the paper is to lay out a general…

Numerical Analysis · Mathematics 2021-07-09 Claude Brezinski , Stefano Cipolla , Michela Redivo-Zaglia , Yousef Saad

Anderson acceleration is a well-established and simple technique for speeding up fixed-point computations with countless applications. Previous studies of Anderson acceleration in optimization have only been able to provide convergence…

Optimization and Control · Mathematics 2020-06-16 Vien V. Mai , Mikael Johansson

The alternating direction method of multipliers (ADMM) has found widespread use in solving separable convex optimization problems. In this paper, by employing Nesterov extrapolation technique, we propose two families of accelerated…

Optimization and Control · Mathematics 2024-05-13 X. He , N. J. Huang , Y. P. Fang

We extend the key idea behind the generalized Petviashvili method of Ref. \cite{gP} by proposing a novel technique based on a similar idea. This technique systematically eliminates from the iteratively obtained solution a mode that is…

Pattern Formation and Solitons · Physics 2009-11-13 T. I. Lakoba , J. Yang

First-order operator splitting methods are ubiquitous among many fields through science and engineering, such as inverse problems, signal/image processing, statistics, data science and machine learning, to name a few. In this paper, we…

Optimization and Control · Mathematics 2020-09-10 Clarice Poon , Jingwei Liang

Acceleration of first order methods is mainly obtained via inertial techniques \`a la Nesterov, or via nonlinear extrapolation. The latter has known a recent surge of interest, with successful applications to gradient and proximal gradient…

Machine Learning · Statistics 2021-10-29 Quentin Bertrand , Mathurin Massias

We propose a simple algebraic method for generating classes of traveling wave solutions for a variety of partial differential equations of current interest in nonlinear science. This procedure applies equally well to equations which may or…

Pattern Formation and Solitons · Physics 2010-04-20 Dionisio Bazeia , Ashok Das , Laercio Losano , Mauro Jose dos Santos

This paper presents a way to define, classify and accelerate the order of convergence of an uncountable family of fractional fixed point methods, which may be useful to continue expanding the applications of fractional operators. The…

Numerical Analysis · Mathematics 2024-03-27 A. Torres-Hernandez , F. Brambila-Paz , R. Montufar-Chaveznava

Despite their frequent slow convergence, proximal gradient schemes are widely used in large-scale optimization tasks due to their tremendous stability, scalability, and ease of computation. In this paper, we develop and investigate a…

Computation · Statistics 2025-08-19 Nicholas C. Henderson , Ravi Varadhan
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