Numerical Experience with a Class of Self-Scaling Quasi-Newton Algorithms |
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Authors: | Al-Baali M |
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Institution: | (1) Department of Mathematics and Computer Science, Faculty of Science, UAE University, Al-Ain, United Ara |
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Abstract: | Self-scaling quasi-Newton methods for unconstrained optimization depend upon updating the Hessian approximation by a formula which depends on two parameters (say, and ) such that = 1, = 0, and = 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. |
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Keywords: | Unconstrained optimization quasi-Newton methods inexact line searches global and superlinear convergence Broyden family self-scaling methods |
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