Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice

2002
ISBN: 978-1-4020-7173-7
Verlag: Springer US

Buch, Englisch, Band 53, 111 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 383 g

Reihe: International Series in Operations Research & Management Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Bienstock, Daniel

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
• Mathematik | Informatik Mathematik Operations Research Spieltheorie
• Mathematik | Informatik Mathematik Mathematische Analysis Variationsrechnung
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik

Inhaltsverzeichnis

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