Spectral Methods: Algorithms, Analysis and Applications: 41 (Springer Series in Computational Mathematics, 41) [Hardcover] [Aug 25, 2011] Shen, Jie; Tang, Tao and Wang, Li-Lian

Along with finite differences and finite elements, spectral methods are one of the three main methodologies for solving partial differential equations on computers. This book provides a detailed presentation of basic spectral algorithms, as well as a systematical presentation of  basic convergence theory and error analysis for spectral methods. Readers of this book will be exposed to a unified framework for designing and analyzing spectral algorithms for a variety of problems, including in particular high-order differential equations and problems in unbounded domains. The book contains a large number of figures which are designed to illustrate various concepts stressed in the book. A set of basic matlab codes has been made available online to help the readers to develop their own spectral codes for their specific applications.

Jie Shen: Ph.D., Numerical Analysis, Universite de Paris-Sud, Orsay, France, 1987; B.S., Computational Mathematics, Peking University, China, 1982.

Professor of Mathematics at Purdue University; Guest Professorships in Shanghai University and Xiamen University; Member of editorial boards for numerous top research journals.

Tao Tang:  Ph.D., Applied Mathematics, University of Leeds, 1989;
Computational Mathematics, Peking University, China, 1984.

Head and  Chair Professor of Hong Kong Baptist University; Cheung Kong Chair  Professor under Ministry of Education of China; Winner of a Leslie Fox Prize in 1988 and a Feng Kang Prize in Scientific Computing in  2003; Member of editorial boards for numerous  top research journals.

Lilian Wang: Ph.D, Computational Mathematics, Shanghai University, China  2000; B.S., Mathematics Education,  Hunan University of Science and Technology, China, 1995.

Assistant Professor of Mathematics,  Nanyang Technological University, Singapore. A  prolific researcher with over twenty research papers in top journals.