Mathematical Programming

4.9k papers and 204.4k indexed citations i.

About

The 4.9k papers published in Mathematical Programming in the last decades have received a total of 204.4k indexed citations. Papers published in Mathematical Programming usually cover Computational Theory and Mathematics (2.9k papers), Numerical Analysis (2.3k papers) and Computational Mechanics (798 papers) specifically the topics of Advanced Optimization Algorithms Research (2.3k papers), Optimization and Variational Analysis (1.3k papers) and Sparse and Compressive Sensing Techniques (677 papers). The most active scholars publishing in Mathematical Programming are Jorge Nocedal, Andreas Wächter, Lorenz T. Biegler, Yu. Nesterov, M. J. D. Powell, Cheng‐Di Dong, Jong‐Shi Pang, Jorge J. Morè, Arkadi Nemirovski and Dimitri P. Bertsekas.

In The Last Decade

Fields of papers published in Mathematical Programming

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Mathematical Programming. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Mathematical Programming.

Countries where authors publish in Mathematical Programming

Since Specialization
Citations

This map shows the geographic impact of research published in Mathematical Programming. 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 papers published in Mathematical Programming with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathematical Programming more than expected).

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.

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