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

Daniel Bienstock

ISBN 10: 1475776721 / ISBN 13: 9781475776720

Editore: Springer US, Springer US, 2013

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9781475776720

Dati bibliografici

• Titolo: Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice
• Editore: Springer US, Springer US
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 1475776721
• ISBN 13: 9781475776720
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 8 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 8 millimetri di profondità
• Peso: 219 grammi

Descrizione articolo

Riassunto

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.

Contenuti

List of Figures. List of Tables. Preface. 1. Introduction. 1. Early Algorithms. 2. The Exponential Potential Function - Key Ideas. 3. Recent Developments. 4. Computational Experiments. Appendices. Index.