New Variable Metric Methods for Unconstrained Minimization Covering the Large-Scale Case

Jan Vlcek (vlcekcs.cas.cz)
Ladislav Luksan (luksancs.cas.cz)

Abstract: A new family of numerically efficient variable metric or quasi-Newton methods for unconstrained minimization are given, which give simple possibility of adaptation for large-scale optimization. Global convergence of the methods can be established for convex sufficiently smooth functions. Some encouraging numerical experience is reported.

Keywords: Unconstrained minimization, variable metric methods, limited-memory

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Unconstrained Optimization )

Citation: Report V876, Institute of Computer Science, AV CR, Pod Vodarenskou Vezi 2, 18207 Praha 8, Czech Republic. Last revision: November 2002.

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Entry Submitted: 11/21/2002
Entry Accepted: 11/21/2002
Entry Last Modified: 11/21/2002

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