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Branch-and-Model: a derivative-free global optimization algorithm

Overview of attention for article published in Computational Optimization and Applications, March 2023
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

twitter
2 X users

Citations

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3 Dimensions

Readers on

mendeley
4 Mendeley
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Title
Branch-and-Model: a derivative-free global optimization algorithm
Published in
Computational Optimization and Applications, March 2023
DOI 10.1007/s10589-023-00466-3
Authors

Kaiwen Ma, Luis Miguel Rios, Atharv Bhosekar, Nikolaos V. Sahinidis, Sreekanth Rajagopalan

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 25%
Student > Doctoral Student 1 25%
Unknown 2 50%
Readers by discipline Count As %
Chemical Engineering 1 25%
Nursing and Health Professions 1 25%
Unknown 2 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 April 2023.
All research outputs
#15,631,278
of 23,854,458 outputs
Outputs from Computational Optimization and Applications
#85
of 338 outputs
Outputs of similar age
#214,668
of 400,162 outputs
Outputs of similar age from Computational Optimization and Applications
#2
of 34 outputs
Altmetric has tracked 23,854,458 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 338 research outputs from this source. They receive a mean Attention Score of 1.4. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 400,162 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.