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Springer Linear Programming: Mathematics, Theory and Algorithms - Springer

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Description

Linear Programming provides an in-depth exploration of simplex-based and interior point techniques for solving linear programming problems. The text begins with a comprehensive review of the mathematical foundations underlying these approaches, then details the primal and dual simplex methods alongside primal-dual, composite, and steepest edge simplex algorithms. Subsequent chapters examine interior point techniques, including projective and affine potential reduction, primal and dual affine scaling, and path following algorithms. This volume, part of the Applied Optimization series, combines rigorous theory with practical algorithmic solutions, making it an essential reference for advanced students and professionals in operations research, mathematical optimization, and applied mathematics seeking a thorough understanding of linear programming methodology.

Key Features

Covers both simplex-based and modern interior point techniques for solving linear programming problems effectively.

Includes detailed exposition of primal and dual simplex methods with primal-dual, composite, and steepest edge variants.

Explores interior point techniques such as projective and affine potential reduction algorithms.

Part of Springer's Applied Optimization series, volume 2, providing comprehensive mathematical theory and practical solutions.

Bridges mathematical foundations and algorithmic implementation for advanced study in operations research and optimization.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 December 1995
Listed Since
15 December 2006

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