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GCMMA-MMA-Python: Python implementation of the Method of Moving Asymptotes

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 13 Oct 2019Publisher:Zenodo
Authors: Deetman, Arjen;
Code repository: https://github.com/arjendeetman/GCMMA-MMA-Python/tree/0.2.0

doi: 10.5281/zenodo.13197566 , 10.5281/zenodo.13198131 , 10.5281/zenodo.13337495 , 10.5281/zenodo.15459165

GCMMA-MMA-Python: Python implementation of the Method of Moving Asymptotes

Abstract

This record contains the Python implementation of the Method of Moving Asymptotes (MMA), originally developed and written in MATLAB by Krister Svanberg. The MMA algorithm is used for solving non-linear programming problems. Users of this code are encouraged to inform Krister Svanberg of their application and intentions via email, as provided on his website. When publishing work that uses this code, please cite Krister Svanberg's original academic work.

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Keywords

python, gcmma, convex approximation, non-linear programming, optimization algorithm, method of moving asymptotes, mma

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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