提交給數字線性代數應用的手稿應該包括大規模的廣泛的應用，其中具有挑戰性的計算結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。數值線性代數與應用程序接收提交地址的地區發展,分析和應用線性代數算法解決問題中出現多重線性代數(張量)的統計數據,如馬爾可夫鏈,以及大規模網絡的確定性和隨機建模,算法開發、性能分析或相關計算方面。主題包括:標準和廣義共軛梯度，多重網格和其他迭代方法;預處理方法;直接的解決方法;特征問題的數值方法;非線性方程牛頓法;數值線性代數中的并行和可向量化算法數值線性代數方法在科學、工程和經濟學中的應用。

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.