Optimization Online


Limited-memory projective variable metric methods for unconstrained minimization

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

Abstract: A new family of limited-memory variable metric or quasi-Newton methods for unconstrained minimization is given. The methods are based on a positive definite inverse Hessian approximation in the form of the sum of identity matrix and two low rank matrices, obtained by the standard scaled Broyden class update. To reduce the rank of matrices, various projections are used. Numerical experience is encouraging.

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

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Unconstrained Optimization )

Citation: Technical report No. V 1036, Institute of Computer Science, Pod Vodarenskou Vezi 2, 18207 Praha 8. December 2008

Download: [Postscript][PDF]

Entry Submitted: 02/25/2009
Entry Accepted: 02/25/2009
Entry Last Modified: 02/25/2009

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Programming Society