JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION
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doi.org/w15463522 →Countries where authors are citing JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION
This map shows the geographic impact of JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION more than expected).
Fields of papers citing JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION
This network shows the impact of JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION.
About JuMP: A MODELING LANGUAGE FOR MATHEMATICAL OPTIMIZATION
This paper, published in 2016, received 1.0k indexed citations . Written by Iain Dunning, Joey Huchette and Miles Lubin covering the research area of Computer Networks and Communications, Management Science and Operations Research and Hardware and Architecture. It is primarily cited by scholars working on Electrical and Electronic Engineering (380 citations), Control and Systems Engineering (284 citations), Management Science and Operations Research (131 citations), Computational Theory and Mathematics (120 citations) and Industrial and Manufacturing Engineering (99 citations).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w15463522.