Agradecimientos:
Catarina P. Avelino was partially supported by Portuguese FCT postdoctoral grant
SFRH/BPD/20453/2004 and by the Research Unit CM-UTAD of University of Trás-os-Montes e Alto
Douro. Javier M. Moguerza and Alberto Olivares were partially supported by Spanish grant MEC
MTM2006-14961-C05-05.
Francisco J. Prieto was partially supported by grant MTM2007-63140 of the Spanish Ministry of
Education.
The aim of this paper is the study of different approaches to combine and
scale, in an efficient manner, descent information for the solution of unconstrained
optimization problems. We consider the situation in which different directions are
available in a givThe aim of this paper is the study of different approaches to combine and
scale, in an efficient manner, descent information for the solution of unconstrained
optimization problems. We consider the situation in which different directions are
available in a given iteration, and we wish to analyze how to combine these directions
in order to provide a method more efficient and robust than the standard Newton
approach. In particular, we will focus on the scaling process that should be carried
out before combining the directions. We derive some theoretical results regarding
the conditions necessary to ensure the convergence of combination procedures following
schemes similar to our proposals. Finally, we conduct some computational experiments to compare these proposals with a modified Newton’s method and other
procedures in the literature for the combination of information.[+][-]
Nota:
The original publication is available at www.springerlink.com
