TY - JOUR
T1 - Wide interval for efficient self-scaling quasi-Newton algorithms
AU - Al-Baali, Mehiddin
AU - Khalfan, Humaid
PY - 2005/12
Y1 - 2005/12
N2 - This article uses certain conditions for the global and superlinear convergence of the two-parameter self-scaling Broyden family of quasi-Newton algorithms for unconstrained optimization to derive a wide interval for self-scaling updates. Numerical testing shows that such algorithms not only accelerate the convergence of the (unscaled) methods from the so-called convex class, but also increase their chances of success. Self-scaling updates from the preconvex and postconvex classes are shown to be effective in practice, and new algorithms, which work well in practice with or without scaling, are also obtained from the new interval. Unlike the behavior of unscaled methods, numerical testing shows that varying the updating parameter in the proposed interval has little effect on the performance of the self-scaling algorithms.
AB - This article uses certain conditions for the global and superlinear convergence of the two-parameter self-scaling Broyden family of quasi-Newton algorithms for unconstrained optimization to derive a wide interval for self-scaling updates. Numerical testing shows that such algorithms not only accelerate the convergence of the (unscaled) methods from the so-called convex class, but also increase their chances of success. Self-scaling updates from the preconvex and postconvex classes are shown to be effective in practice, and new algorithms, which work well in practice with or without scaling, are also obtained from the new interval. Unlike the behavior of unscaled methods, numerical testing shows that varying the updating parameter in the proposed interval has little effect on the performance of the self-scaling algorithms.
KW - Broyden's family
KW - Global and superlinear convergence
KW - Quasi-Newton methods
KW - Self-scaling
KW - Unconstrained optimization
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U2 - 10.1080/10556780410001709448
DO - 10.1080/10556780410001709448
M3 - Article
AN - SCOPUS:29744452629
SN - 1055-6788
VL - 20
SP - 679
EP - 691
JO - Optimization Methods and Software
JF - Optimization Methods and Software
IS - 6
ER -