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Foundations of Mathematical Optimization : Convex Analysis without Linearity - Diethard Pallaschke

Foundations of Mathematical Optimization

Convex Analysis without Linearity

Hardcover

Published: 28th February 1997
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Many books on optimization consider only finite dimensional spaces. This volume is unique in its emphasis: the first three chapters develop optimization in spaces without linear structure, and the analog of convex analysis is constructed for this case. Many new results have been proved specially for this publication. In the following chapters optimization in infinite topological and normed vector spaces is considered. The novelty consists in using the drop property for weak well-posedness of linear problems in Banach spaces and in a unified approach (by means of the Dolecki approximation) to necessary conditions of optimality. The method of reduction of constraints for sufficient conditions of optimality is presented. The book contains an introduction to non-differentiable and vector optimization.
Audience: This volume will be of interest to mathematicians, engineers, and economists working in mathematical optimization.

Preface
General Optimality
Optimization in Metric Spaces
Multifunctions and Marginal Functions in Metric Spaces
Well-Posedness and Weak Well-Posedness in Banach Spaces
Duality in Banach and Hilbert Spaces. Regularization
Necessary Conditions for Optimality and Local Optimality in Normed Spaces
Polynomials. Necessary and Sufficient Conditions of Optimality of Higher Order
Nondifferentiable Optimization
Numerical Aspects
Vector Optimization
Bibliography
Subject index
Author index
List of symbols
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780792344247
ISBN-10: 0792344243
Series: MATHEMATICS AND ITS APPLICATIONS (KLUWER )
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 585
Published: 28th February 1997
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 25.4 x 17.15  x 4.45
Weight (kg): 1.2