Numerical Experience with a Class of Self-Scaling Quasi-Newton Algorithms

M. Al-Baali
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M. Al-Baali: UAE University

Journal of Optimization Theory and Applications, 1998, vol. 96, issue 3, No 3, 533-553

Abstract: Abstract Self-scaling quasi-Newton methods for unconstrained optimization depend upon updating the Hessian approximation by a formula which depends on two parameters (say, τ and θ) such that τ = 1, θ = 0, and θ = 1 yield the unscaled Broyden family, the BFGS update, and the DFP update, respectively. In previous work, conditions were obtained on these parameters that imply global and superlinear convergence for self-scaling methods on convex objective functions. This paper discusses the practical performance of several new algorithms designed to satisfy these conditions.

Keywords: Unconstrained optimization; quasi-Newton methods; inexact line searches; global and superlinear convergence; Broyden family; self-scaling methods (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1023/A:1022608410710

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