A Modified Quasi-Newton Method for Structured Optimization with Partial Information on the Hessian

  • Authors:

  • L. H. Chen;N. Y. Deng;J. Z. Zhang

  • Affiliations:

  • Guanghua School of Management, Peking University, Beijing, China 100080;Division of Basic Science, Agricultural University of China, China;Department of Mathematics, City University of Hong Kong, Hong Kong

  • Venue:
  • Computational Optimization and Applications
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper develops a modified quasi-Newton method for structured unconstrained optimization with partial information on the Hessian, based on a better approximation to the Hessian in current search direction. The new approximation is decided by both function values and gradients at the last two iterations unlike the original one which only uses the gradients at the last two iterations. The modified method owns local and superlinear convergence. Numerical experiments show that the proposed method is encouraging comparing with the methods proposed in [4] for structured unconstrained optimization