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A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems

Overview of attention for article published in Mathematical Programming, March 2018
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1 X user

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29 Mendeley
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Title
A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems
Published in
Mathematical Programming, March 2018
DOI 10.1007/s10107-018-1253-9
Authors

Natashia Boland, Jeffrey Christiansen, Brian Dandurand, Andrew Eberhard, Fabricio Oliveira

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 41%
Researcher 5 17%
Student > Doctoral Student 3 10%
Professor > Associate Professor 2 7%
Student > Master 2 7%
Other 1 3%
Unknown 4 14%
Readers by discipline Count As %
Engineering 8 28%
Mathematics 4 14%
Computer Science 3 10%
Business, Management and Accounting 2 7%
Environmental Science 1 3%
Other 4 14%
Unknown 7 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 February 2017.
All research outputs
#20,400,885
of 22,950,943 outputs
Outputs from Mathematical Programming
#589
of 678 outputs
Outputs of similar age
#292,513
of 330,901 outputs
Outputs of similar age from Mathematical Programming
#9
of 9 outputs
Altmetric has tracked 22,950,943 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 678 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 330,901 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one.