-

 

 

 




Optimization Online





 

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

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

Download: [Postscript]

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

Modify/Update this entry


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

 

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