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Introduction to Combinatorial Optimization

Title
Introduction to Combinatorial Optimization [electronic resource] / by Ding-Zhu Du, Panos M. Pardalos, Xiaodong Hu, Weili Wu.
ISBN
9783031105968
Edition
1st ed. 2022.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2022.
Physical Description
XI, 402 p. 144 illus., 132 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
Introductory courses in combinatorial optimization are popular at the upper undergraduate/graduate levels in computer science, industrial engineering, and business management/OR, owed to its wide applications in these fields. There are several published textbooks that treat this course and the authors have used many of them in their own teaching experiences. This present text fills a gap and is organized with a stress on methodology and relevant content, providing a step-by-step approach for the student to become proficient in solving combinatorial optimization problems. Applications and problems are considered via recent technology developments including wireless communication, cloud computing, social networks, and machine learning, to name several, and the reader is led to the frontiers of combinatorial optimization. Each chapter presents common problems, such as minimum spanning tree, shortest path, maximum matching, network flow, set-cover, as well as key algorithms, such as greedy algorithm, dynamic programming, augmenting path, and divide-and-conquer. Historical notes, ample exercises in every chapter, strategically placed graphics, and an extensive bibliography are amongst the gems of this textbook.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
June 23, 2023
Series
Springer Optimization and Its Applications, 196
Springer Optimization and Its Applications, 196
Contents
1. Introduction.-2. Divide-and-Conquer
3. Dynamic Programming and Shortest Path
4. Greedy Algorithm and Spanning Tree
5. Incremental Method and Maximum Network Flow
6. Linear Programming
7. Primal-Dual Methods and Minimum Cost Flow
8. NP-hard Problems and Approximation Algorithms
9. Restriction and Steiner Tree
10. Greedy Approximation and Submodular Optimization
11. Relaxation and Rounding. 12. Nonsubmodular Optimization
Bibliography.
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