Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Meeting Abstract

Solving Large-Scale Nonnegative Least Squares

MPG-Autoren
/persons/resource/persons76142

Sra,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

http://www.dm.unipi.it/~ilas2010/pdf/abstracts-all.pdf
(Zusammenfassung)

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)

ilas_[0].pdf
(beliebiger Volltext), 330KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Sra, S. (2010). Solving Large-Scale Nonnegative Least Squares. In 16th Conference of the International Linear Algebra Society (ILAS 2010) (pp. 67).


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-C004-E
Zusammenfassung
We study the fundamental problem of nonnegative least squares. This problem
was apparently introduced by Lawson and Hanson [1] under the name NNLS.
As is evident from its name, NNLS seeks least-squares solutions that are also
nonnegative. Owing to its wide-applicability numerous algorithms have been
derived for NNLS, beginning from the active-set approach of Lawson and Han-
son [1] leading up to the sophisticated interior-point method of Bellavia et al. [2].
We present a new algorithm for NNLS that combines projected subgradients
with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our
resulting algorithm is called BBSG, and we guarantee its convergence by ex-
ploiting properties of NNLS in conjunction with projected subgradients. BBSG
is surprisingly simple and scales well to large problems. We substantiate our
claims by empirically evaluating BBSG and comparing it with established con-
vex solvers and specialized NNLS algorithms. The numerical results suggest
that BBSG is a practical method for solving large-scale NNLS problems.