Foundations of Mathematical Optimization: Convex Analysis without Linearity
Author:   Diethard Ernst Pallaschke ,  S. Rolewicz
Publisher:   Springer
Edition:   1997 ed.
Volume:   388
ISBN:  

9780792344247


Pages:   585
Publication Date:   28 February 1997
Format:   Hardback
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Foundations of Mathematical Optimization: Convex Analysis without Linearity


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Overview

Many books on optimization consider only finite dimensional spaces. This volume has a different 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.

Full Product Details

Author:   Diethard Ernst Pallaschke ,  S. Rolewicz
Publisher:   Springer
Imprint:   Springer
Edition:   1997 ed.
Volume:   388
Dimensions:   Width: 15.60cm , Height: 3.30cm , Length: 23.40cm
Weight:   2.250kg
ISBN:  

9780792344247


ISBN 10:   0792344243
Pages:   585
Publication Date:   28 February 1997
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

1. General Optimality.- 2. Optimization in Metric Spaces.- 3. Multifunctions and Marginal Functions in Metric Spaces.- 4. Well-Posedness and Weak Well-Posedness in Banach Spaces.- 5. Duality in Banach and Hilbert Spaces. Regularization.- 6.Necessary Conditions for Optimality and Local Optimality in Normed Spaces.- 7.Polynomials. Necessary and Sufficient Conditions of Optimality of Higher Order.- 8. Nondifferentiable Optimization.- 9. Numerical Aspects.- 10. Vector Optimization.- Author index.- List of symbols.

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