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.

提交給數(shù)字線性代數(shù)應(yīng)用的手稿應(yīng)該包括大規(guī)模的廣泛的應(yīng)用，其中具有挑戰(zhàn)性的計算結(jié)果是研究和分析方法的組成部分。編輯認為不符合這些條件的稿件將不予接受審查。數(shù)值線性代數(shù)與應(yīng)用程序接收提交地址的地區(qū)發(fā)展,分析和應(yīng)用線性代數(shù)算法解決問題中出現(xiàn)多重線性代數(shù)(張量)的統(tǒng)計數(shù)據(jù),如馬爾可夫鏈,以及大規(guī)模網(wǎng)絡(luò)的確定性和隨機建模,算法開發(fā)、性能分析或相關(guān)計算方面。主題包括:標準和廣義共軛梯度，多重網(wǎng)格和其他迭代方法;預(yù)處理方法;直接的解決方法;特征問題的數(shù)值方法;非線性方程牛頓法;數(shù)值線性代數(shù)中的并行和可向量化算法數(shù)值線性代數(shù)方法在科學(xué)、工程和經(jīng)濟學(xué)中的應(yīng)用。