Trading off 1-norm and sparsity against rank for linear models using mathematical optimization: 1-norm minimizing partially reflexive ah-symmetric generalized inverses

Trading off 1-norm and sparsity against rank for linear models using mathematical optimization: 1-norm minimizing partially reflexive ah-symmetric generalized inverses

The M-P (Moore–Penrose) pseudoinverse has as a key application the computation of least-squares solutions of inconsistent systems of linear equations. Irrespective of whether a given input matrix is sparse, its M-P pseudoinverse can be dense, potentially leading to high computational burden, especia...

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Bibliographic Details
Main Authors: Fampa, Marcia, Lee, Jon, Ponte, Gabriel
Format: Article
Language:English
Published: Université de Montpellier 2021-06-01
Series:Open Journal of Mathematical Optimization
Subjects:
Moore–Penrose pseudoinverse
generalized inverse
linear model
least squares
sparse optimization
low rank
Online Access:https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.6/
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https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.6/

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