Self-Scaling Variable Metric Algorithms Without Line Search for Unconstrained Minimization

@inproceedings{Oren2007SelfScalingVM,
  title={Self-Scaling Variable Metric Algorithms Without Line Search for Unconstrained Minimization},
  author={Shmuel S. Oren},
  year={2007}
}
This paper introduces a new class of quasi-Newton algorithms for unconstrained minimization in which no line search is necessary and the inverse Hessian approximations are positive definite. These algorithms are based on a two-parameter family of rank two, updating formulae used earlier with line search in self-scaling variable metric algorithms. It is proved that, in a quadratic case, the new algorithms converge at least weak superlinearly. A special case of the above algorithms was… CONTINUE READING
Highly Cited
This paper has 34 citations. REVIEW CITATIONS