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.File | Size | Format | |
---|---|---|---|
HAL-Random projections for semidefinite programming.pdf Open Access from 17/07/2024
Type: versione post-print
Size 308.93 kB
Format Adobe PDF
|
308.93 kB | Adobe PDF | View/Open |
liberti-random_projections_for_semidefinite_programming.pdf Solo gestori archivio
Type: versione editoriale
Size 172.14 kB
Format Adobe PDF
|
172.14 kB | Adobe PDF | & nbsp; View / Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.