Potential Function Methods For Approximately Solving Linear Programming Problems Theory And Practice
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Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice
Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground in linear programming theory. The book draws on the research developments in three broad areas: linear and integer programming, numerical analysis, and the computational architectures which enable speedy, high-level algorithm design. During the last ten years, a new body of research within the field of optimization research has emerged, which seeks to develop good approximation algorithms for classes of linear programming problems. This work both has roots in fundamental areas of mathematical programming and is also framed in the context of the modern theory of algorithms. The result of this work, in which Daniel Bienstock has been very much involved, has been a family of algorithms with solid theoretical foundations and with growing experimental success. This book will examine these algorithms, starting with some of the very earliest examples, and through the latest theoretical and computational developments.
Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice
Author: Daniel Bienstock
language: en
Publisher: Springer Science & Business Media
Release Date: 2002-08-31
Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground in linear programming theory. The book draws on the research developments in three broad areas: linear and integer programming, numerical analysis, and the computational architectures which enable speedy, high-level algorithm design. During the last ten years, a new body of research within the field of optimization research has emerged, which seeks to develop good approximation algorithms for classes of linear programming problems. This work both has roots in fundamental areas of mathematical programming and is also framed in the context of the modern theory of algorithms. The result of this work, in which Daniel Bienstock has been very much involved, has been a family of algorithms with solid theoretical foundations and with growing experimental success. This book will examine these algorithms, starting with some of the very earliest examples, and through the latest theoretical and computational developments.
Integer Programming and Combinatorial Optimization
Author: Michael Jünger
language: en
Publisher: Springer Science & Business Media
Release Date: 2005-06
This book constitutes the refereed proceedings of the 11th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2005, held in Berlin, Germany in June 2005. The 34 revised full papers presented were carefully reviewed and selected from 119 submissions. Among the topics addressed are mixed-integer programming, graph theory, graph algorithms, approximation, linear programming, approximability, packing, scheduling, computational geometry, randomization, network algorithms, sequencing, TSP, and travelling salesman problem.