| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 285 | 17 | 23 | 10 |
| Computer Science | 581 | 23 | 28 | 10 |
| Engineering and Technology | 1051 | 12 | 12 | 7 |
The journal primarily focuses on research topics in Theory of computation, Mathematical optimization, Algorithm, Integer programming and Solver. Research in Combinatorics and the interrelating topic of Discrete mathematics were among the subjects of interest in the Theory of computation studies discussed in Mathematical Programming Computation. The work tackled in it goes beyond the discipline of Mathematical optimization as it also encompasses Integer (computer science).
The Algorithm study featured in Mathematical Programming Computation draws parallels with the field of Benchmark (computing). The studies on Integer programming discussed can also contribute to research in the domains of Theoretical computer science, Rounding and Relaxation (approximation). The work on Relaxation (approximation) tackled in it brings together disciplines like Semidefinite programming and Cutting-plane method.
Mathematical Programming Computation explores topics in Solver which can be helpful for research in disciplines like Set (abstract data type), Speedup and Global optimization. The journal focuses on Interior point method but sometimes tackles the closely related topic of Matrix (mathematics) which is concerned with Applied mathematics. The main emphasis of Mathematical Programming Computation is the research on Linear programming, emphasizing the topic of Linear programming relaxation.
The published articles tackle a plethora of topics, such as Mathematical optimization, Theory of computation, Algorithm, Solver and Semidefinite programming. Quadratic equation and Integer (computer science) are some topics wherein Mathematical optimization research discussed in the journal papers has an impact. The published articles tackle research in various disciplines, including Theory of computation and Line search.
Mathematical Programming Computation mostly deals with topics like Theory of computation, Mathematical optimization, Solver, Integer programming and Integer (computer science). Theory of computation research presented in the journal encompasses a variety of subjects, including Kullback–Leibler divergence, Signomial, Polynomial optimization, Heuristics and Subgradient method. While work presented in the journal provided substantial information on Mathematical optimization, it also covered topics in Fraction (mathematics), Graph (abstract data type) and Variable (mathematics).
Mathematical Programming Computation explores research in Optimization problem and overlapping concepts in Quadratic equation, Quadratic programming, Constrained optimization, Matrix decomposition and Block (programming) to expand the discourse in Solver. The studies in Integer programming featured incorporate elements of Tree (graph theory), Rounding and Power graph analysis. The concepts on Integer (computer science) presented in it can also apply to other research fields, including Structure (category theory), Theoretical computer science, Nonlinear programming, Data-driven and Implementation.
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 Mathematical Programming Computation (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 Mathematical Programming Computation (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, 4.76% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 50.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 30.00% of all publications and 10.00% 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.
Bartolomeo Stellato;Goran Banjac;Paul Goulart;Alberto Bemporad
(2020)Ambros M. Gleixner;Gregor Hendel;Gerald Gamrath;Tobias Achterberg
(2021)Ashutosh Mahajan;Sven Leyffer;Jeff T. Linderoth;James R. Luedtke
(2021)Riley Murray;Venkat Chandrasekaran;Adam Wierman
(2021)Artur M. Schweidtmann;Dominik Bongartz;Daniel Grothe;Tim Kerkenhoff
(2021)Bernard Knueven;James Ostrowski;Jean-Paul Watson
(2020)For those interested in advancing their education in Computer Science, exploring online doctorate programs can be a strategic choice. These programs offer flexibility and depth, enabling learners to balance their studies with professional commitments while gaining expert-level knowledge.
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Finally, understanding future trends is vital. Insights into the best college majors for the future emphasize the importance of interdisciplinary skills and emerging technologies, guiding students toward areas with sustained demand and growth potential.
