文件名称：How to Think About Algorithms
文件大小：6.04MB
文件格式：PDF
更新时间：2011-10-19 04:13:29
算法
Publisher: Cambridge University Press Publication: 2008, English ISBN: 9780521849319 Pages: 472 There are many algorithm texts that provide lots of well-polished code and proofs of correctness. This book is not one of them. Instead, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author helps students avoid the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. Part of the goal is to teach the students to think abstractly. Without getting bogged with formal proofs, the book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a slow and clear manner accessible to second- or third-year students of computer science, preparing them to find their own innovative ways to solve problems. Rather than provide lots of well-polished code and proofs of correctness, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. It stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. About the Author Jeff Edmonds received his Ph.D. in 1992 at University of Toronto in theoretical computer science. His thesis proved that certain computation problems require a given amount of time and space. He did his postdoctorate work at the ICSI in Berkeley on secure multi-media data transmission and in 1995 became an Associate Professor in the Department of Computer Science at York University, Canada. He has taught their algorithms course thirteen times to date. He has worked extensively at IIT Mumbai, India, and University of California San Diego. He is well published in the top theoretical computer science journals in topics including complexity theory, scheduling, proof systems, probability theory, combinatorics, and, of course, algorithms.