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Numerical Linear Algebra with Applications in Data Analysis
Special Issue Information
Dear Colleagues,
Linear algebra deals with vector and matrices, which allows us to compactly manipulate data objects. On the one hand, vector and matrices are used in many mathematical theories: probability, statistics, optimization, learning, etc.; on the other hand, numerical linear algebra is used in many applications: solving linear systems of equations, linear regression in data classification, principal component analysis in data analysis, inversion of the Hessian matrix in the Newton–Raphson optimization method, ranking individuals using their social network interaction graph data, etc. Innovative numerical linear algebra tools are still needed to cope with the large amounts of data encountered in today’s life applications.
The purpose of this Special Issue is to gather a collection of articles reflecting new trends in numerical linear algebra with applications in data analysis. Some topics of interest are: vector space, matrix decomposition or factorization, accuracy, efficient computation, modeling, numerical stability and convergence. We welcome original research papers and review articles related to numerical linear algebra in the broad sense.
Prof. Dr. Doulaye Dembélé
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- vector spaces, range and subspace
- vector and matrix norms
- linear system of equations
- least squares and total least squares
- eigenvalues and singular values
- matrix decomposition methods
- positive and nonnegative matrices
- stochastic matrices
- special matrices and applications
- large-scale systems and sparse matrices
- direct and iterative methods
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