Practical Performance of Random Projections in Linear Programming
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.
Linear Programming
Johnson-Lindenstrauss Lemma
Computational testing
Mathematics of computing~Mathematical optimization
Theory of computation~Random projections and metric embeddings
21:1-21:15
Regular Paper
https://mega.nz/file/p8MQhbpT#0TJBUVgaBf4KPVk2fu_5k05cMy2VozJk-0fQ1PZdJ0U
Leo
Liberti
Leo Liberti
LIX CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France
www.lix.polytechnique.fr/ liberti
https://orcid.org/0000-0003-3139-6821
Benedetto
Manca
Benedetto Manca
Department of Mathematics and Informatics, University of Cagliari, Italy
https://orcid.org/0000-0003-0209-0655
Partly supported by grant STAGE, Fondazione Sardegna 2018.
Pierre-Louis
Poirion
Pierre-Louis Poirion
RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
https://orcid.org/0000-0002-3783-3036
10.4230/LIPIcs.SEA.2022.21
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Leo Liberti, Benedetto Manca, and Pierre-Louis Poirion
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