Foundations of Mathematical Optimization Convex Analysis without Linearity
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...
| Main Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Dordrecht
Springer Netherlands
1997, 1997
|
| Edition: | 1st ed. 1997 |
| Series: | Mathematics and Its Applications
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
| Summary: | 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 |
|---|---|
| Physical Description: | XII, 585 p online resource |
| ISBN: | 9789401715881 |