Product details:
| ISBN13: | 9781009349185 |
| ISBN10: | 100934918X |
| Binding: | Paperback |
| No. of pages: | 575 pages |
| Size: | 237x183x23 mm |
| Weight: | 710 g |
| Language: | English |
| 816 |
Category:
Online Algorithms
Publisher: Cambridge University Press
Date of Publication: 16 November 2023
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Short description:
A rigorous and comprehensive introduction to online algorithms in a pedagogy-rich, readily accessible form for students.
Long description:
Online algorithms are a rich area of research with widespread applications in scheduling, combinatorial optimization, and resource allocation problems. This lucid textbook provides an easy but rigorous introduction to online algorithms for graduate and senior undergraduate students. In-depth coverage of most of the important topics is presented with special emphasis on elegant analysis. The book starts with classical online paradigms like the ski-rental, paging, list-accessing, bin packing, where performance of online algorithms is studied under the worst-case input and moves on to newer paradigms like 'beyond worst case', where online algorithms are augmented with predictions using machine learning algorithms. The book goes on to cover multiple applied problems such as routing in communication networks, server provisioning in cloud systems, communication with energy harvested from renewable sources, and sub-modular partitioning. Finally, a wide range of solved examples and practice exercises are included, allowing hands-on exposure to the concepts.
Table of Contents:
Preface; Acknowledgements; Notations; Chapter 1. Introduction; Chapter 2. Ski-Rental; Chapter 3. List Accessing; Chapter 4. Bin-Packing; Chapter 5. Paging; Chapter 6. Metrical Task System; Chapter 7. Secretary Problem; Chapter 8. Knapsack; Chapter 9. Bipartite Matching; Chapter 10. Primal-Dual Technique; Chapter 11. Facility Location and k-Means Clustering; Chapter 12. Load Balancing; Chapter 13. Scheduling to Minimize Flow Time (Delay); Chapter 14. Scheduling with Speed Scaling; Chapter 15. Scheduling to Minimize Energy with Job Deadlines; Chapter 16. Travelling Salesman; Chapter 17. Convex Optimization (Server Provisioning in Cloud Computing); Chapter 18. Multi-Commodity Flow Routing; Chapter 19. Resource Constrained Scheduling (Energy Harvesting Communication); Chapter 20. Submodular Partitioning for Welfare Maximization; Appendix 1. Types of Adversaries and Their Relationships; Appendix 2. KKT Conditions for Convex Optimization Problems; Bibliography; Index.
