Foundations of Mathematical Optimization : Convex Analysis without Linearity

Diethard Ernst Pallaschke

ISBN 10: 0792344243 / ISBN 13: 9780792344247

Published by Springer, Springer, 1997

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9780792344247

Bibliographic Details

• Title: Foundations of Mathematical Optimization : Convex Analysis without Linearity
• Publisher: Springer, Springer
• Publication Date: 1997
• Language: English
• ISBN 10: 0792344243
• ISBN 13: 9780792344247
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 38D millimeters 241 millimeters width by 160 millimeters height by 38 millimeters depth
• Weight: 1062 grams

About this title

Synopsis

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.