| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 203 | 44 | 89 | 12 |
| Computer Science | 721 | 15 | 24 | 7 |
The scientific interests tackled in the journal are Applied mathematics, Mathematical analysis, Mathematical optimization, Matrix (mathematics) and Linear system. Applied mathematics research featured in the journal incorporates concerns from various other topics such as Finite element method, Iterative method, Preconditioner, Multigrid method and Eigenvalues and eigenvectors. Finite element method study tackled is connected to the field of Discretization.
Krylov subspace is a major topic of Iterative method research. It links adjacent topics like Preconditioner with Conjugate gradient method. While Numerical Linear Algebra With Applications focused on Multigrid method, it was also able to explore topics like Grid and Solver.
The research on Mathematical analysis tackled can also make contributions to studies in the areas of Rate of convergence and Domain decomposition methods. Combinatorics and Rank (linear algebra) are some topics wherein Matrix (mathematics) research discussed in it have an impact. The studies on Linear system discussed can also contribute to research in the domains of Positive-definite matrix and Algorithm.
The most cited publications aim to foster the development of research in Applied mathematics, Mathematical analysis, Mathematical optimization, Algebra and Multigrid method. While Applied mathematics is the focus of the most cited papers, it also provides insights into the studies of Positive-definite matrix, Matrix (mathematics) and Linear system, Preconditioner, Krylov subspace. The journal articles explore topics in Mathematical analysis which can be helpful for research in disciplines like Domain decomposition methods, Iterative method, Conjugate gradient method, Matrix splitting and Eigenvalues and eigenvectors.
Numerical Linear Algebra With Applications is organized to address concerns in the fields of Applied mathematics, Multigrid method, Algorithm, Iterative method and Matrix (mathematics). Issues in Applied mathematics were discussed, taking into consideration concepts from other disciplines like Krylov subspace, Reduction (complexity), Preconditioner, Parareal and Discretization. It addresses concerns in the field of Preconditioner by exploring it in line with topics in Robustness (computer science) which intersect with Linear system and Factorization subjects.
While Numerical Linear Algebra With Applications mainly focused on Multigrid method studies, it also tackled the scientific discipline of interrelated fields such as
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Numerical Linear Algebra With Applications (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Numerical Linear Algebra With Applications (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 1.25% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.78% were posted by at least one author from the top 10 institutions publishing in the journal. Another 3.80% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.78% of all publications and 50.63% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Guangjing Song;Michael K. Ng;Xiongjun Zhang
(2020)Oscar Mickelin;Sertac Karaman
(2020)Lothar Reichel;Ugochukwu O. Ugwu
(2021)Abdulkarim Hassan Ibrahim;Poom Kumam;Poom Kumam;Auwal Bala Abubakar;Auwal Bala Abubakar;Abubakar Adamu;Abubakar Adamu
(2021)Hans De Sterck;Robert D. Falgout;Stephanie Friedhoff;Oliver A. Krzysik
(2021)Giovanni Barbarino;Stefano Serra‐Capizzano;Stefano Serra‐Capizzano
(2020)Haniye Dehestani;Yadollah Ordokhani;Mohsen Razzaghi
(2021)Chao Zeng;Michael K. Ng
(2020)For those pursuing a career in Computer Science, exploring related online degrees can broaden opportunities and specialize skill sets. Many students choose online programs for their flexibility and affordability. For instance, if you have an interest in broad technical fields, consider online engineering degrees which offer foundational knowledge applicable in various tech roles.
Creative technology fields are also gaining popularity. Aspiring developers in interactive entertainment may find earning a masters in game design online a strategic choice for entering the booming gaming industry.
With cybersecurity threats on the rise, a specialized cyber security masters is increasingly valuable for protecting organizational data and infrastructure from attacks.
Data-driven decision making powers today's businesses, making a best data science master's programs ideal for those looking to analyze and interpret complex datasets for actionable insights.
