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AuthorBienstock, Daniel. author
TitlePotential Function Methods for Approximately Solving Linear Programming Problems [electronic resource] : Theory and Practice / by Daniel Bienstock
ImprintBoston, MA : Springer US, 2002
Connect tohttp://dx.doi.org/10.1007/b115460
Descript XIX, 111 p. online resource

SUMMARY

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


CONTENT

Early Algorithms -- The Exponential Potential Function - key Ideas -- Recent Developments -- Computational Experiments Using the Exponential Potential Function Framework


Mathematics Operations research Decision making Mathematical optimization Calculus of variations Management science Mathematics Calculus of Variations and Optimal Control; Optimization Optimization Operations Research Management Science Operation Research/Decision Theory



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