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Generalizations of the limited-memory BFGS method based on quasi-product form of update

Jan Vlcek (vlcek***at***cs.cas.cz )
Ladislav Luksan (luksan***at***cs.cas.cz)

Abstract: Two families of limited-memory variable metric or quasi-Newton methods for unconstrained minimization based on quasi-product form of update are derived. As for the first family, four variants how to utilize the Strang recurrences for the Broyden class of variable metric updates are investigated; three of them use the same number of stored vectors as the limited-memory BFGS method. Moreover, one of the variants does not require any additional matrix by vector multiplication. The second family uses vectors from the preceding iteration to construct a new class of variable metric updates. Resulting methods again require neither any additional matrix by vector multiplication nor any additional stored vector. Global convergence of four of presented methods is established for convex sufficiently smooth functions. Numerical results indicate that two of the new methods can save computational time substantially for certain problems.

Keywords: Unconstrained minimization, variable metric methods, limited-memory methods, Broyden class updates, global convergence, numerical results.

Category 1: Nonlinear Optimization

Citation: Report V-1060, Institute of Computer Science AS CR, Pod Vodarenskou Vezi 2, 18207 Prague 8, 2009.

Download: [PDF]

Entry Submitted: 11/25/2010
Entry Accepted: 11/25/2010
Entry Last Modified: 11/26/2010

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