块边界非线性系统的Feng-Schnabel算法的一些理论性质

IF 1.4 3区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Optimization Methods & Software Pub Date : 1999-01-01 DOI:10.1080/10556789908805741
G. Zanghirati
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引用次数: 1

摘要

大型和稀疏非线性系统出现在许多科学和技术领域,经常作为一个现实世界问题模型的核心过程。类似牛顿的解决方法意味着计算一个(可能近似的)雅可比矩阵:在块边界系统的情况下,这导致在主对角线上有不相交的方形块的矩阵,加上最后一组行和列。这个稀疏性类允许开发类似牛顿的多阶段方法(具有内部和外部迭代),非常适合多处理器计算机的并行实现。最近,冯和施纳贝尔提出了一个算法,这实际上是该领域的最新技术。本文深入分析了Feng-Schnabel算法生成的步长的重要理论性质。然后,我们研究了一种廉价的修正,该修正改进了方向性质,允许全局收敛结果,并将收敛扩展到更广泛的算法类别,…
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Some theoretical properties of Feng-Schnabel algorithm for block bordered nonlinear systems
Large and sparse nonlinear systems arise in many areas of science and technology, very often as a core process for the model of a real world problem. Newton-like approaches to their solution imply the computation of a (possibly approximated) Jacobian: in the case of block bordered systems this results in a matrix with disjoint square blocks on the main diagonal, plus a final set of rows and columns. This sparsity class allows to develop multistage Newton-like methods (with inner and outer iterations) that are very suitable for a parallel implementation ou multiprocessors computers. Recently, Feng and Schnabel proposed an algorithm which is actually the state of the art in this field. In this paper we analyze in depth important theoretical properties of the steps generated by the Feng-Schnabel algorithm. Then we study a cheap modification that gives an improvement of the direction properties, allowing a global convergence result, as well as the extension of the convergence to a broader class of algorithms,...
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来源期刊
Optimization Methods & Software
Optimization Methods & Software 工程技术-计算机:软件工程
CiteScore
4.50
自引率
0.00%
发文量
40
审稿时长
7 months
期刊介绍: Optimization Methods and Software publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design. Topics include: Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms. Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators. Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.
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