Optimization Online


Recursive formulation of limited memory variable metric methods

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

Abstract: In this report we propose a new recursive matrix formulation of limited memory variable metric methods. This approach can be used for an arbitrary update from the Broyden class (and some other updates) and also for the approximation of both the Hessian matrix and its inverse. The new recursive formulation requires approximately $4 m n$ multiplications and additions per iteration, so it is comparable with other efficient limited memory variable metric methods. Numerical experiments concerning Algorithm~1, proposed in this report, confirm its practical efficiency.

Keywords: Unconstrained optimization, large scale optimization, limited memory met\-hods, variable metric updates, recursive matrix formulation, algorithms.

Category 1: Nonlinear Optimization

Citation: Report V-1059, Institute of Computer Science AS CR, Pod Vodarenskou Vezi 2, 18207 Prague 8, 2010.

Download: [PDF]

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

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