| Potential Function Methods for Approximately Solving Linear Programming Problems: Theory and Practice 2002 Edition Contributor(s): Bienstock, Daniel (Author) |
|
 |
ISBN: 1402071736 ISBN-13: 9781402071737 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: August 2002 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Linear & Nonlinear Programming - Medical - Computers | Programming - General |
| Dewey: 519.72 |
| LCCN: 2002073008 |
| Series: International Operations Research & Management Science |
| Physical Information: 0.53" H x 6.36" W x 9.76" (0.81 lbs) 111 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |