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Extra-Updates Criterion for the Limited Memory BFGS Algorithm for Large Scale Nonlinear Optimization

M. Al-Baali (albaali***at***squ.edu.om)

Abstract: This paper studies recent modifications of the limited memory BFGS (L-BFGS) method for solving large scale unconstrained optimization problems. Each modification technique attempts to improve the quality of the L-BFGS Hessian by employing (extra) updates in certain sense. Because at some iterations these updates might be redundant or worsen the quality of this Hessian, this paper proposes an updates criterion to measure this quality. Hence, extra updates are employed only to improve the poor approximation of the L-BFGS Hessian. The presented numerical results illustrate the usefulness of this criterion and show that extra updates improve the performance of the L-BFGS method substantially.

Keywords: large scale optimization, quasi-Newton methods, limited memory BFGS method.

Category 1: Nonlinear Optimization (Unconstrained Optimization )

Citation: N. 27, Department of Mathematics, Statistics, Informatics and Applications, Bergamo University, Via Salvecchio, 19 - 24129, Bergamo, Italy, December/2000.

Download: [PDF]

Entry Submitted: 04/11/2001
Entry Accepted: 04/11/2001
Entry Last Modified: 04/11/2001

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