£108.78

Springer Foundations of Mathematical Optimization - Vol 388

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Description

Expand your understanding of advanced mathematics with Foundations of Mathematical Optimization: Convex Analysis without Linearity. Published by Springer as part of the Mathematics and Its Applications series, this volume provides a unique perspective on optimization theory. While many standard texts focus solely on finite dimensional spaces, this book introduces optimization in spaces without linear structure during its first three chapters. This text builds a foundation for convex analysis in these non-linear environments, presenting many new results developed specifically for this publication. Readers will explore optimization within infinite topological and normed vector spaces. The work introduces novel methodologies, such as using the drop property for the weak well-posedness of linear problems in Banach spaces. It also presents a unified approach through the Dolecki approximation. This book serves as a specialized resource for those studying applied mathematics, offering deep insights into complex mathematical structures and new theoretical frameworks.

Key Features

Explores optimization in spaces without linear structure to provide a unique perspective beyond standard finite dimensional approaches.

Includes many new results proved specifically for this publication to support advanced mathematical research.

Covers optimization in infinite topological and normed vector spaces for a comprehensive study of mathematical theory.

Utilizes the drop property for the weak well-posedness of linear problems in Banach spaces.

Provides a unified approach to mathematical problems through the use of the Dolecki approximation.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
07 December 2010
Listed Since
20 September 2010

Barcode

No barcode data available

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