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Springer Foundations of Mathematical Optimization - Convex Analysis

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Description

Foundations of Mathematical Optimization offers a distinctive approach to optimisation theory by developing analysis in spaces without linear structure, constructing the analogue of convex analysis for these cases. This 388-page volume from Springer's Mathematics and Its Applications series presents many results proved exclusively for this publication. The work progresses from optimisation in non-linear spaces through to infinite topological and normed vector spaces, employing the drop property for weak well-posedness of linear problems in Banach spaces and utilising the unified Dolecki approximation approach. This comprehensive treatment bridges the gap left by traditional optimisation texts that focus solely on finite dimensional spaces, making it essential for advanced mathematicians and researchers seeking rigorous foundations beyond conventional linear frameworks.

Key Features

Develops optimisation theory in spaces without linear structure, moving beyond the limitations of finite dimensional approaches covered in standard texts.

Constructs the analogue of convex analysis for non-linear spaces, with many new results proved exclusively for this Springer publication.

Covers optimisation in infinite topological and normed vector spaces using the drop property for weak well-posedness of linear problems in Banach spaces.

Employs the unified Dolecki approximation method to provide a coherent framework across diverse optimisation problems and spaces.

Part of Springer's Mathematics and Its Applications series (volume 388), offering peer-reviewed mathematical rigour for advanced study.

Product Specifications

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

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