Self-scaling variable metric algorithms without line search for unconstrained minimization

Author:
Shmuel S. Oren

Journal:
Math. Comp. **27** (1973), 873-885

MSC:
Primary 65K05

Corrigendum:
Math. Comp. **28** (1974), 887.

Corrigendum:
Math. Comp. **28** (1974), 887.

MathSciNet review:
0329259

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Abstract | References | Similar Articles | Additional Information

Abstract: 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 implemented and tested numerically on several test functions. In this implementation, however, cubic interpolation was performed whenever the objective function was not satisfactorily decreased on the first "shot" (with unit step size), but this did not occur too often, except for very difficult functions. The numerical results indicate that the new algorithm is competitive and often superior to previous methods.

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Additional Information

DOI:
https://doi.org/10.1090/S0025-5718-1973-0329259-8

Keywords:
Function minimization,
unconstrained minimization,
quasi-Newton methods,
variable metric methods,
self-scaling variable metric algorithms,
scaling,
quasi-Newton algorithms with line search,
gradient methods,
Hessian matrix inverse approximation,
conditioning of search methods,
convergence rates

Article copyright:
© Copyright 1973
American Mathematical Society