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首页> 外文期刊>Wiley interdisciplinary reviews. Computational statistics >Sparse matrix computations with application to solve system of nonlinear equations
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Sparse matrix computations with application to solve system of nonlinear equations

机译:稀疏矩阵计算及其在求解非线性方程组中的应用

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摘要

Numerical linear algebra is an essential ingredient in algorithms for solving problems in optimization, nonlinear equations, and differential equations. Spanning diverse application areas, from economic planning to complex network analysis, modeling and solving problems arising in those areas share a common theme: numerical calculations on matrices that are sparse or structured or both. Linear algebraic calculations involving sparse matrices of order 109 are now routine. In this article,we give an overview of scientific calculations where effective utilization of properties such as sparsity, problem structure, etc. play a vital role and where the linear algebraic calculations are much more complex than their dense counterpart. This is partly because operation and storage involving known zeros must be avoided, and partly because the fact that modern computing hardware may not be amenable to the specialized techniques needed for sparse problems. We focus on sparse calculations arising in nonlinear equation solving using the Newton method.
机译:数值线性代数是算法中解决优化,非线性方程和微分方程问题的重要组成部分。从经济计划到复杂的网络分析,跨越不同的应用领域,建模和解决这些领域中出现的问题具有一个共同的主题:对稀疏或结构化矩阵或两者进行矩阵的数值计算。现在涉及109阶稀疏矩阵的线性代数计算是常规的。在本文中,我们概述了科学计算,其中稀疏性,问题结构等属性的有效利用起着至关重要的作用,线性代数计算比稠密对应要复杂得多。部分原因是必须避免涉及已知零的运算和存储,部分原因是现代计算硬件可能不适合稀疏问题所需的专门技术。我们专注于使用牛顿法求解非线性方程的稀疏计算。

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