Computational experience with numerical methods for nonnegative least‐squares problems | Zendy

Bellavia Stefania | Zendy; Gondzio Jacek | Zendy; Morini Benedetta | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Computational experience with numerical methods for nonnegative least‐squares problems

Author(s) -

Bellavia Stefania,

Gondzio Jacek,

Morini Benedetta

Publication year - 2011

Publication title -

numerical linear algebra with applications

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.02

H-Index - 53

eISSN - 1099-1506

pISSN - 1070-5325

DOI - 10.1002/nla.732

Subject(s) - mathematics , affine transformation , computation , least squares function approximation , scaling , mathematical optimization , scale (ratio) , non linear least squares , algorithm , linear least squares , estimation theory , statistics , physics , geometry , quantum mechanics , estimator , pure mathematics , singular value decomposition

We discuss the solution of large‐scale box‐constrained linear least‐squares problems by two recent affine‐scaling methods: a cyclic Barzilai–Borwein strategy and an Inexact Newton‐like method where a preconditioning technique allows for an efficient computation of the steps. A robust globally and fast locally convergent method based on the combination of the two procedures is presented along with extensive numerical results. Copyright © 2010 John Wiley & Sons, Ltd.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research