Linear and Integer Optimization : Theory and Practice, Third Edition

Gerard Sierksma

ISBN 10: 1498710166 / ISBN 13: 9781498710169

Published by CRC Press Mai 2015, 2015

About this Item

Neuware - This textbook presents a strong and clear relationship between theory and practice. It covers basic topics such as Dantzig's simplex algorithm, duality, sensitivity analysis, integer optimization models, and network models as well as more advanced topics including interior point algorithms, the branch-and-bound algorithm, cutting planes, and complexity. Along with case studies, it also discusses more advanced techniques such as column generation, multiobjective optimization, and game theory. It also includes computer code in the form of models in GMPL. The book contains appendices covering mathematical proofs, linear algebra, graph theory, convexity, and a background in nonlinear optimization. All chapters contain extensive examples and exercises.

Seller Inventory # 9781498710169

Bibliographic Details

• Title: Linear and Integer Optimization : Theory and Practice, Third Edition
• Publisher: CRC Press Mai 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 1498710166
• ISBN 13: 9781498710169
• Binding: Buch
• Edition: 3rd Edition
• Condition: Neu
• Dimensions: 254W x 180H x 38D millimeters 254 millimeters width by 180 millimeters height by 38 millimeters depth
• Weight: 1610 grams

About this title

Synopsis

Presenting a strong and clear relationship between theory and practice, Linear and Integer Optimization: Theory and Practice is divided into two main parts. The first covers the theory of linear and integer optimization, including both basic and advanced topics. Dantzig’s simplex algorithm, duality, sensitivity analysis, integer optimization models, and network models are introduced.

More advanced topics also are presented including interior point algorithms, the branch-and-bound algorithm, cutting planes, complexity, standard combinatorial optimization models, the assignment problem, minimum cost flow, and the maximum flow/minimum cut theorem.

The second part applies theory through real-world case studies. The authors discuss advanced techniques such as column generation, multiobjective optimization, dynamic optimization, machine learning (support vector machines), combinatorial optimization, approximation algorithms, and game theory.

Besides the fresh new layout and completely redesigned figures, this new edition incorporates modern examples and applications of linear optimization. The book now includes computer code in the form of models in the GNU Mathematical Programming Language (GMPL). The models and corresponding data files are available for download and can be readily solved using the provided online solver.

This new edition also contains appendices covering mathematical proofs, linear algebra, graph theory, convexity, and nonlinear optimization. All chapters contain extensive examples and exercises. This textbook is ideal for courses for advanced undergraduate and graduate students in various fields including mathematics, computer science, industrial engineering, operations research, and management science.

About the Author

Gerard Sierksma, PhD, University of Groningen, The Netherlands
Yori Zwols, PhD, Google UK, London