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About this book
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.
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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
- complexity
Table of contents (6 chapters)
Bibliographic Information
Book Title: The Newton-Cauchy Framework
Book Subtitle: A Unified Approach to Unconstrained Nonlinear Minimization
Editors: J. L. Nazareth
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-57671-1
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1994
Softcover ISBN: 978-3-540-57671-6Published: 28 February 1994
eBook ISBN: 978-3-540-48310-6Published: 01 July 2005
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XII, 108
Topics: Mathematics of Computing, Computer-Aided Engineering (CAD, CAE) and Design, Numerical Analysis, IT in Business, Complexity, Systems Theory, Control