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On the generation of metric TSP instances with a large integrality gap by branch-and-cut

Overview of attention for article published in Mathematical Programming Computation, March 2023
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)

Mentioned by

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5 X users

Citations

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3 Dimensions

Readers on

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6 Mendeley
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Title
On the generation of metric TSP instances with a large integrality gap by branch-and-cut
Published in
Mathematical Programming Computation, March 2023
DOI 10.1007/s12532-023-00235-7
Authors

Eleonora Vercesi, Stefano Gualandi, Monaldo Mastrolilli, Luca Maria Gambardella

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 33%
Unknown 4 67%
Readers by discipline Count As %
Unspecified 2 33%
Unknown 4 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 03 November 2023.
All research outputs
#7,203,250
of 23,565,002 outputs
Outputs from Mathematical Programming Computation
#21
of 86 outputs
Outputs of similar age
#87,195
of 296,203 outputs
Outputs of similar age from Mathematical Programming Computation
#1
of 1 outputs
Altmetric has tracked 23,565,002 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 86 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 75% 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 296,203 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them