| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 76 | 65 | 90 | 20 |
| Engineering and Technology | 481 | 38 | 52 | 17 |
| Computer Science | 574 | 27 | 35 | 10 |
The aim of Optimization Methods & Software is to expand the discussion of research in Mathematical optimization, Algorithm, Applied mathematics, Convergence (routing) and Nonlinear programming. The journal focuses on Mathematical optimization but the discussions also offer insight into other areas such as Convex optimization and Nonlinear system. It features Algorithm research that overlaps with concepts in Set (abstract data type).
The studies in Applied mathematics featured incorporate elements of Mathematical analysis and Conjugate gradient method. It addresses concerns in Conjugate gradient method which are intertwined with other disciplines, such as Gradient descent and Derivation of the conjugate gradient method. It tackles topics on Derivation of the conjugate gradient method, which can potentially contribute to the wider field of Conjugate residual method.
Many of the studies tackled connect Interior point method with a similar field of study like Semidefinite programming. It dives deep in exploring the relationship between the study of Semidefinite programming and Semidefinite embedding.
The most cited publications tackle a plethora of topics, such as Mathematical optimization, Applied mathematics, Algorithm, Interior point method and Nonlinear system. While the journal articles focused on Mathematical optimization, they were also able to explore topics like Convergence (routing), Nonlinear programming and Convex optimization. The most cited papers with studies in Interior point method featured incorporate elements of Linear programming, Second-order cone programming and Semidefinite programming.
Optimization Methods & Software is organized to address concerns in the fields of Mathematical optimization, Applied mathematics, Algorithm, Convex optimization and Interior point method. The journal tackles studies in Game theory and the interrelated subject of Submodular set function to gain insights into Mathematical optimization. It holds forums on Applied mathematics that merges themes from other disciplines such as Rate of convergence, Simple (abstract algebra), Eigenvalues and eigenvectors and Regular polygon.
The journal discusses concepts in Combinatorial optimization under Algorithm and how they intertwine with disciplines like Resolution (electron density). It explores issues in Convex optimization which can be linked to other research areas like Minification, Type (model theory), Convex function, Bounded function and Variational inequality. While work presented in it provided substantial information on Interior point method, it also covered topics in Neighbourhood (graph theory), Regularization (mathematics), Semidefinite programming and Constrained optimization.
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 Optimization Methods & Software (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 Optimization Methods & Software (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, 3.92% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.20% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.12% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.37% of all publications and 65.31% 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.
Nikolaus Hansen;Anne Auger;Raymond Ros;Olaf Mersmann
(2021)Hassan Rafique;Mingrui Liu;Qihang Lin;Tianbao Yang
(2021)César A. Uribe;Soomin Lee;Alexander V. Gasnikov;Angelia Nedic
(2021)Albert S. Berahas;Raghu Bollapragada;Jorge Nocedal
(2020)Defeng Sun;Kim-Chuan Toh;Yancheng Yuan;Xin-Yuan Zhao
(2020)Yurii Nesterov;Alexander Gasnikov;Sergey Guminov;Pavel Dvurechensky
(2020)Exploring online degrees in Computer Science offers a flexible pathway to enter a high-demand, lucrative field. Many students seek programs that balance affordability with quality, making the cheapest online college options particularly attractive. These programs help reduce the financial burden while delivering a solid foundation in key technical skills.
For those eager to accelerate their education, accelerated degree programs enable students to complete their studies faster and enter the workforce sooner. This is ideal for career changers or professionals aiming to boost their credentials without a lengthy time commitment.
Additionally, selecting the right institution is crucial. Many reputable schools now offer options with no added barriers, such as those listed among the online college no application fee category, which reduces upfront costs and simplifies enrollment.
Ultimately, pursuing a degree in Computer Science aligns well with the data on most lucrative college majors, offering strong salary potential and diverse career pathways in technology and innovation.
