Unlike other books on theoretical computer science, this textbook organizes approximation algorithms into chapters based on the design techniques for the algorithms. This allows the reader to study approximation algorithms of the same nature together.

This book is intended to be used as a textbook for graduate students studying theoretical computer science. It can also be used as a reference book for researchers in the area of design and analysis of approximation algorithms. Design and Analysis of Approximation Algorithms is a graduate course in theoretical computer science taught widely in the universities, both in the United States and abroad. There are, however, very few textbooks available for this course. Among those available in the market, most books follow a problem-oriented format; that is, they collected many important combinatorial optimization problems and their approximation algorithms, and organized them based on the types, or applications, of problems, such as geometric-type problems, algebraic-type problems, etc. Such arrangement of materials is perhaps convenient for a researcher to look for the problems and algorithms related to his/her work, but is difficult for a student to capture the ideas underlying the various algorithms. In the new book proposed here, we follow a more structured, technique-oriented presentation. We organize approximation algorithms into different chapters, based on the design techniques for the algorithms, so that the reader can study approximation algorithms of the same nature together. It helps the reader to better understand the design and analysis techniques for approximation algorithms, and also helps the teacher to present the ideas and techniques of approximation algorithms in a more unified way.

The technique-oriented approach provides a unified view of the design techniques for approximation algorithms Detailed algorithms, as well as complete proofs and analyses, are presented for each technique Numerous examples help the reader to better understand the design and analysis techniques Collects a great number of applications, many from recent research papers Includes a large collection of approximation algorithms of geometric problems Includes supplementary material: sn.pub/extras

Autorentext

Ding-Zhu Du is Professor of Computer Science at the University of Texas at Dallas. For a number of years he was the Editor-in-Chief of the Journal of Combinatorial Optimization and the combinatorial optimization series editor for the SOIA book series. Professor Du is co-editor of the first and second editions of the Handbook of Combinatorial Optimization. He was also co-author (with Pardalos and Wu) of the Kluwer publication "Mathematical Theory of Optimization." Panos M. Pardalos is Distinguished Professor Emeritus of Industrial and Systems Engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor in Industrial & Systems Engineering. He is also an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the Director of the Center for Applied Optimization. Dr. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing. He has co-authored and co-edited more than 30 books, as well as publishing more than 600 journal articles and conference proceedings. Prof. Pardalos is a Fellow of AAAS (American Association for the Advancement of Science), Fellow of American Institute for Medical and Biological Engineering (AIMBE), and EUROPT. He is a Distinguished International Professor by the Chinese Minister of Education; Honorary Professor of Anhui University of Sciences and Technology, China; Elizabeth Wood Dunlevie Honors Term Professor; Honorary Doctor, V.M. Glushkov Institute of Cybernetics of The National Academy of Sciences of Ukraine; Foreign Associate Member of Reial Academia de Doctors, Spain; and Advisory board member of the Centre for Optimisation and Its Applications, Cardiff University, UK. He is also the recipient of UF 2009 International Educator Award; Medal (in recognition of broad contributions in science and engineering) of the University of Catani, Italy; EURO Gold Medal (EGM); Honorary Doctor of Science Degree, Wilfrid Laurier University, Canada; Senior Fulbright Specialist Award; University of Florida Research Foundation Professorship; and IBM Achievement Award. Xiaodong Hu is a research professor at the Institute of Applied Mathematics, Chinese Academy of Sciences. He was the president of OR society of China a few year ago. His research interests include combinatorial optimization, and approximation algorithms, to name just two. Weili Wu is pra ofessor of computer science at the University of Texas at Dallas. Her research interests include optimization theory, big data management and analysis, social networks, database systems, and wireless sensor networks, to name just several. She has published more than200 journal papers and 100 conference papers. Especially, she made several influential contributions in the study of wireless sensor networks, which are accumulated in a Springer publication "Optimal Coverage in Wireless Sensor Networks".

Klappentext

When precise algorithmic solutions are difficult to compute, the use of approximation algorithms can help. Design and Analysis of Approximation Algorithms is a textbook for a graduate course in theoretical computer science taught globally in universities. It can also be used as a reference work for researchers in the area of design and analysis algorithms.

There are few texts available for this standard course, and those that do exist mainly follow a problem-oriented format. This text follows a structured, technique-oriented presentation. Approximation algorithms are organized into chapters based on the design techniques for the algorithms, enabling the reader to study algorithms of the same nature with ease, and providing an improved understanding of the design and analysis techniques for approximation algorithms. Instructors benefit from this approach allowing for an easy way to present the ideas and techniques of algorithms with a unified approach.

Inhalt
Preface.- 1. Introduction.- 2. Greedy Strategy.- 3. Restriction.- 4. Partition.- 5. Guillotine Cut.- 6. Relaxation.- 7. Linear Programming.- 8. Primal-Dual Scheme and Local Ratio.- 9. Semidefinite Programming.- 10. Inapproximability.- Bibliography.- Index.