Local convergence analysis of proximal Gauss–Newton method for penalized nonlinear least squares problems

• Published in: Applied mathematics and computation Vol. 241; pp. 401 - 408
• Main Authors: Argyros, Ioannis K., Magreñán, Á. Alberto
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.08.2014
• Subjects: Center majorant function; Computational efficiency; Convergence; Error analysis; Gauss-Newton method; Hilbert space; Least squares method; Least squares problems; Majorant function; Mathematical analysis; Mathematical models; Nonlinearity; Proximal Newton–Gauss methods; Proximal Newton–Gauss methods; Majorant function; Least squares problems; Hilbert space; Center majorant function
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2014.04.087

We present a local convergence analysis of the proximal Gauss–Newton method for solving penalized nonlinear least squares problems in a Hilbert space setting. Using more precise majorant conditions than in earlier studies such as (Allende and Gonçalves) [1], (Ferreira et al., 2011) [9] and a combination of a majorant and a center majorant function, we provide: a larger radius of convergence; tighter error estimates on the distances involved and a clearer relationship between the majorant function and the associated least squares problem. Moreover, these advantages are obtained under the same computational cost as in earlier studies using only the majorant function.