This book describes a simple and powerful method that is iterative in essence, and similarly useful in a variety of settings for exact and approximate optimization. The authors highlight the commonality and uses of this method to prove a variety of classical polyhedral results on matchings, trees, matroids, and flows.
About the Author
Lap-Chi Lau is an Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong. Lap-Chi's main research interests are in combinatorial optimization and graph algorithms. His paper on Steiner tree packing was given the Machtey award in the IEEE Foundations of Computer Science Conference. His Ph.D. thesis was awarded the Doctoral Prize from the Canadian Mathematical Society and a Doctoral Prize from the Natural Sciences and Engineering Research Council of Canada.
R. Ravi is Carnegie Bosch Professor of Operations Research and Computer Science at Carnegie Mellon University. Ravi's main research interests are in combinatorial optimization (particularly in approximation algorithms), computational molecular biology and electronic commerce. He is currently on the editorial boards of Management Science and the ACM Transactions on Algorithms.
Mohit Singh is an Assistant Professor in the School of Computer Science, McGill University. He completed his Ph.D. in 2008 at the Tepper School of Business, Carnegie Mellon University, where his advisor was Professor R. Ravi. His thesis was awarded the Tucker prize by the Mathematical Programming Society. His research interests include approximation algorithms, combinatorial optimization and studying models that deal with uncertainty in data.