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Table of contents

About this book

Introduction

Computational unconstrained nonlinear optimization comes to life from a study of the interplay between the metric-based (Cauchy) and model-based (Newton) points of view. The motivating problem is that of minimizing a convex quadratic function. This research monograph reveals for the first time the essential unity of the subject. It explores the relationships between the main methods, develops the Newton-Cauchy framework and points out its rich wealth of algorithmic implications and basic conceptual methods. The monograph also makes a valueable contribution to unifying the notation and terminology of the subject. It is addressed topractitioners, researchers, instructors, and students and provides a useful and refreshing new perspective on computational nonlinear optimization.

Keywords

Gradient Method Gradientenverfahren Mathematical Programming Mathematisches Programmieren Motivation Newton-Cauchy Method Newton-Cauchy Verfahren Nichtlineare Optimierung Unbeschränkte Minimierung Unconstrained Minimization algorithm algorithms linear optimization nonlinear optimization optimization

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-57671-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 1994
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-57671-6
  • Online ISBN 978-3-540-48310-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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