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Journal ArticleDOI

Some convergence results for asynchronous algorithms

Mouhamed Nabih El Tarazi
- 01 Oct 1982 - 
- Vol. 39, Iss: 3, pp 325-340
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TLDR
In this article, the authors present convergence results for the asynchronous algorithms based essentially on the notion of classical contraction and generalize all convergence results of those algorithms which are based on the vectorial norm hypothesis.
Abstract
Nous presentons dans cet article des resultats de convergence des algorithmes asynchrones bases essentiellement sur la notion classique de contraction. Nous generalisons, en particulier, tous les resultats de convergence de ces algorithmes qui font l'hypothese de contraction en norme vectorielle qui recemment a ete tres souvant utilisee. Par ailleurs, l'hypothese de contraction en norme vectorielle peut se trouver difficile, voire impossible a verifier pour certains problemes qui peuvent etre cependant abordes dans le cadre de la contraction classique que nous adoptons. In this paper we present convergence results for the asynchronous algorithms based essentially on the notion of classical contraction. We generalize, in particular, all convergence results for those algorithms which are based on the vectorial norm hypothesis, in wide spread use recently. Certain problems, for which the vectorial norm hypothesis can be difficult or even impossible to verify, can nontheless be tackled within the scope of the classical contraction that we adopte.

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Citations
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On asynchronous iterations

TL;DR: Certain models of asynchronous iterations, using a common theoretical framework, are reviewed, including nonsingular linear systems, nonlinear systems, and initial value problems that arise naturally on parallel computers.
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ARock: an Algorithmic Framework for Asynchronous Parallel Coordinate Updates

TL;DR: Theoretically, it is shown that if the nonexpansive operator $T$ has a fixed point, then with probability one, ARock generates a sequence that converges to a fixed points of $T$.
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Some aspects of the parallel and distributed iterative algorithms—a survey

TL;DR: This work considers iterative algorithms of the form x := f ( x ), executed by a parallel or distributed computing system, and considers synchronous executions of such iterations and study their communication requirements, as well as issues related to processor synchronization.
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Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms

TL;DR: In this paper, the convergence rate of distributed augmented Lagrangian (AL) methods has been studied and the dependence of the convergence rates on the underlying network parameters has been established.
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Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms

TL;DR: In this article, the convergence rate of distributed augmented Lagrangian (AL) methods has been analyzed and the dependence of the convergence rates on the underlying network parameters has been shown.
References
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Book

Iterative Solution of Nonlinear Equations in Several Variables

TL;DR: In this article, the authors present a list of basic reference books for convergence of Minimization Methods in linear algebra and linear algebra with a focus on convergence under partial ordering.
Journal ArticleDOI

Matrix Iterative Analysis

Book

Matrix iterative analysis

TL;DR: In this article, the authors propose Matrix Methods for Parabolic Partial Differential Equations (PPDE) and estimate of Acceleration Parameters, and derive the solution of Elliptic Difference Equations.
Journal ArticleDOI

Asynchronous Iterative Methods for Multiprocessors

TL;DR: A class of asynchronous iterative methods is presented for solving a system of equations corresponding to a parallel implementation on a multiprocessor system with no synchronization between cooperating processes to show clearly the advantage of purely asynchronous Iterative methods.