Dynamic scaling on the limited memory BFGS method

Fahimeh Biglari

European Journal of Operational Research, 2015, vol. 243, issue 3, 697-702

Abstract: This paper describes a limited-memory quasi-Newton method in which the initial inverse Hessian approximation is constructed based on the concept of equilibration of the inverse Hessian matrix. Curvature information about the objective function is stored in the form of a diagonal matrix, and plays the dual role of providing an initial matrix and of equilibrating for limited memory BFGS (LBFGS) iterations. An extensive numerical testing has been performed showing that the diagonal scaling strategy proposed is very effective.

Keywords: (B)Large scale optimization; (I)Nonlinear programming; Limited memory quasi-Newton methods; Column scaling; Equilibrated matrix, (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:243:y:2015:i:3:p:697-702

DOI: 10.1016/j.ejor.2014.12.050

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