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提交给数字线性代数应用的手稿应该包括大规模的广泛的应用，其中具有挑战性的计算结果是研究和分析方法的组成部分。编辑认为不符合这些条件的稿件将不予接受审查。数值线性代数与应用程序接收提交地址的地区发展,分析和应用线性代数算法解决问题中出现多重线性代数(张量)的统计数据,如马尔可夫链,以及大规模网络的确定性和随机建模,算法开发、性能分析或相关计算方面。主题包括:标准和广义共轭梯度，多重网格和其他迭代方法;预处理方法;直接的解决方法;特征问题的数值方法;非线性方程牛顿法;数值线性代数中的并行和可向量化算法数值线性代数方法在科学、工程和经济学中的应用。
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Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review.Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects.Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
