Optimization Online


Compact Representations of Structured BFGS Matrices

Johannes Brust (jbrust***at***anl.gov)
Sven Leyffer (leyffer***at***anl.gov)
Cosmin Petra (petra1***at***llnl.gov)
Zichao (Wendy) Di (wendydi***at***mcs.anl.gov)

Abstract: For general large-scale optimization problems compact representations exist in which recursive quasi-Newton update formulas are represented as compact matrix factorizations. For problems in which the objective function contains additional structure, so-called structured quasi-Newton methods exploit available second-derivative information and approximate unavailable second derivatives. This article develops the compact representations of two structured Broyden-Fletcher-Goldfarb-Shanno update formulas. The compact representations enable efficient limited memory and initialization strategies. Two limited memory line search algorithms are described and tested on a collection of problems.

Keywords: Quasi-Newton method, limited memory method, large-scale optimization, compact representation, BFGS method

Category 1: Nonlinear Optimization

Citation: ANL/MCS-P9279-0120, Argonne National Laboratory 9700 South Cass Avenue, 01/2020

Download: [PDF]

Entry Submitted: 01/31/2020
Entry Accepted: 01/31/2020
Entry Last Modified: 08/27/2020

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 Optimization Society