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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SEA.2022.21
URN: urn:nbn:de:0030-drops-165550
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16555/
Liberti, Leo ;
Manca, Benedetto ;
Poirion, Pierre-Louis
Practical Performance of Random Projections in Linear Programming
Abstract
The use of random projections in mathematical programming allows standard solution algorithms to solve instances of much larger sizes, at least approximately. Approximation results have been derived in the relevant literature for many specific problems, as well as for several mathematical programming subclasses. Despite the theoretical developments, it is not always clear that random projections are actually useful in solving mathematical programs in practice. In this paper we provide a computational assessment of the application of random projections to linear programming.
BibTeX - Entry
@InProceedings{liberti_et_al:LIPIcs.SEA.2022.21,
author = {Liberti, Leo and Manca, Benedetto and Poirion, Pierre-Louis},
title = {{Practical Performance of Random Projections in Linear Programming}},
booktitle = {20th International Symposium on Experimental Algorithms (SEA 2022)},
pages = {21:1--21:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-251-8},
ISSN = {1868-8969},
year = {2022},
volume = {233},
editor = {Schulz, Christian and U\c{c}ar, Bora},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/16555},
URN = {urn:nbn:de:0030-drops-165550},
doi = {10.4230/LIPIcs.SEA.2022.21},
annote = {Keywords: Linear Programming, Johnson-Lindenstrauss Lemma, Computational testing}
}
