Computational unconstrained nonlinear optimization comes tolife from a study of the interplay between the metric-based(Cauchy) and model-based (Newton) points of view. Themotivating problem is that of minimizing a convex quadraticfunction. This research monograph reveals for the first timethe essential unity of the subject. It explores therelationships between the main methods, develops theNewton-Cauchy framework and points out its rich wealth ofalgorithmic implications and basic conceptual methods. Themonograph also makes a valueable contribution to unifyingthe notation and terminology of the subject. It is addressedtopractitioners, researchers, instructors, and students andprovides a useful and refreshing new perspective oncomputational nonlinear optimization.
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