Limited memory BFGS method based on a high-order tensor model

Fahimeh Biglari () and Ali Ebadian ()

Computational Optimization and Applications, 2015, vol. 60, issue 2, 413-422

Abstract: This paper is aimed to employ a modified quasi-Newton equation in the framework of the limited memory BFGS method to solve large-scale unconstrained optimization problems. The modified secant equation is derived by means of a forth order tensor model to improve the curvature information of the objective function. The global and local convergence properties of the modified LBFGS method, on uniformly convex problems are also studied. The numerical results indicate that the proposed limited memory method is superior to the standard LBFGS method. Copyright Springer Science+Business Media New York 2015

Keywords: Large scale nonlinear optimization; Modified limited memory quasi-Newton method; Curvature approximation; Modified quasi-Newton equation; R-linear convergence; 90C06; 90C53 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10589-014-9678-4

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