理解算法:从思维到设计

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"How to Think about Algorithms - 一本关于算法设计的经典入门教材,通过思维引导来教授如何设计算法,适合初学者和进阶者。" 在《How to Think about Algorithms》这本书中,作者深入浅出地介绍了算法设计的核心概念,旨在帮助读者理解并掌握算法设计的思维方式。书中的内容涵盖了一些基本的算法设计技术,如循环不变量(Loop Invariants)和递归(Recursion),这些都是理解和构建高效算法的关键。 循环不变量是分析和设计循环结构算法的重要工具,它是指在循环开始前、每次迭代后都保持不变的性质。理解循环不变量可以帮助我们验证算法的正确性,确保循环会在预期的条件下终止,并且能正确地解决问题。例如,在解决数学问题时,如Pooh和主角之间的乘法游戏,利用循环不变量可以清晰地跟踪计算过程,确保答案的准确性。 递归是算法设计的另一个核心概念,尤其是在处理分治策略和树形结构问题时。递归通常涉及函数调用自身,解决小规模问题,然后将结果组合以解决大规模问题。在书中,可能通过讲解经典问题,如汉诺塔(Hanoi Tower)、斐波那契数列(Fibonacci Sequence)等,来阐述递归的工作原理和应用。 此外,书中可能还会介绍其他算法设计技术,如分治法(Divide and Conquer)、动态规划(Dynamic Programming)以及贪心算法(Greedy Algorithm)。这些方法在解决复杂问题时非常有用,例如排序、查找、图论问题等。 书中的例子和寓言,如Pooh和主角的对话,旨在以轻松的方式吸引读者,使抽象的算法概念变得生动有趣。通过这种方式,作者试图激发读者的思考,引导他们以更直观的方式来理解和设计算法。 此外,书中还可能包含针对不同应用场景的章节,以帮助读者将所学知识应用于实际问题。预读部分(Preface)和致教育者与学生(To the Educator and the Student)的部分,可能会提供教学建议和学习策略,以确保读者能够最大限度地从书中获益。 《How to Think about Algorithms》是一本旨在培养读者算法思维的教材,通过各种实例和技巧,使读者不仅能够学会如何编写算法,还能理解算法背后的思想,从而提升问题解决能力。无论是对于计算机科学的学生还是专业开发者,这本书都将是一份宝贵的资源。
2008-09-28 上传
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.