An inexact proximal regularization method for unconstrained optimization

Paul Armand () and Isaï Lankoandé ()
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Paul Armand: XLIM Laboratory – University of Limoges
Isaï Lankoandé: XLIM Laboratory – University of Limoges

Mathematical Methods of Operations Research, 2017, vol. 85, issue 1, No 4, 43-59

Abstract: Abstract We present a regularization algorithm to solve a smooth unconstrained minimization problem.This algorithm is suitable to solve a degenerate problem, when the Hessian is singular at a local optimal solution. The main feature of our algorithm is that it uses an outer/inner iteration scheme. We show that the algorithm has a strong global convergence property under mild assumptions. A local convergence analysis shows that the algorithm is superlinearly convergent under a local error bound condition. Some numerical experiments are reported.

Keywords: Regularized Newton method; Unconstrained optimization (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00186-016-0561-1

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