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Practical Performance of Random Projections in Linear Programming Liberti, Leo Manca, Benedetto Poirion, Pierre-Louis Linear Programming Johnson-Lindenstrauss Lemma Computational testing 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Leo Liberti and Benedetto Manca and Pierre-Louis Poirion 2022 Is Part Of LIPIcs, Volume 233, 20th International Symposium on Experimental Algorithms (SEA 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SEA.2022.21 urn:nbn:de:0030-drops-165550 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.21 eng https://creativecommons.org/licenses/by/4.0/legalcode