Random Projections for Semidefinite Programming

Manca B.;
2023-01-01

Abstract

Random projections can reduce the dimensionality of point sets while keeping approximate congruence. Applying random projections to optimization problems raises many theoretical and computational issues. Most of the theoretical issues in the application of random projections to conic programming were addressed in Liberti et al. (Linear Algebr. Appl. 626:204–220, 2021) [1]. This paper focuses on semidefinite programming.
2023
Inglese
Optimization and Decision Science: Operations Research, Inclusion and Equity
AIRO Springer Series
Georgia Fargetta, et al.
Paola Cappanera, Matteo Lapucci, Fabio Schoen, Marco Sciandrone, Fabio Tardella, Filippo Visintin
9
97
108
12
Springer
Cham
978-3-031-28862-3
978-3-031-28863-0
2-s2.0-85174302232
Esperti anonimi
scientifica
sì
info:eu-repo/semantics/bookPart
2.1 Contributo in volume (Capitolo o Saggio)
Liberti, L.; Manca, B.; Oustry, A.; Poirion, P. -L.
2 Contributo in Volume::2.1 Contributo in volume (Capitolo o Saggio)
4
268
partially_open
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