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＜電子ブック＞
Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice / by Daniel Bienstock
(International Series in Operations Research & Management Science. ISSN:22147934 ; 53)

版	1st ed. 2002.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	2002
本文言語	英語
大きさ	XIX, 111 p : online resource
著者標目	*Bienstock, Daniel author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Calculus of variations LCSH:Operations research LCSH:Management science FREE:Calculus of Variations and Optimization FREE:Optimization FREE:Operations Research, Management Science FREE:Operations Research and Decision Theory
一般注記	Early Algorithms -- The Exponential Potential Function - key Ideas -- Recent Developments -- Computational Experiments Using the Exponential Potential Function Framework 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 HTTP:URL=https://doi.org/10.1007/b115460

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