《算法设计》Kleinberg & Tardos 教材英文版

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"Algorithm.Design,由Kleinberg和Tardos编写的算法设计教材,是国外知名的教学材料,主要探讨算法的设计和分析。该书由Addison-Wesley在2005年出版,包含了丰富的算法理论和实践内容,旨在帮助读者理解和构建高效的计算解决方案。" 本书《Algorithm Design》是算法设计领域的经典之作,由康奈尔大学的两位著名学者Jon Kleinberg和Eva Tardos合作撰写。全书涵盖了广泛的算法主题,包括图论、动态规划、网络流、近似算法以及随机化方法等核心概念。这些算法设计技术对于解决计算机科学中的复杂问题至关重要。 在图论部分,书中深入讲解了图的基本概念,如最短路径算法(Dijkstra's algorithm)和最小生成树(Prim's和Kruskal's algorithms),这些都是理解和解决网络中距离计算问题的基础。同时,书中还涵盖了网络流问题,如Ford-Fulkerson算法,这些方法在优化物流、通信网络等问题中有着广泛的应用。 动态规划是算法设计中的另一个关键工具,它能够解决具有重叠子问题和最优子结构的问题。书中通过经典的背包问题、最长公共子序列等例子,让读者掌握动态规划的思想和应用。 近似算法部分则讨论了如何在无法找到精确解的情况下,寻找问题的接近最优解。这在处理NP-hard问题时尤为重要,例如旅行商问题和最大独立集问题。书中详细介绍了多项式时间内的近似算法设计技巧。 随机化算法是近年来发展迅速的领域,Kleinberg和Tardos在书中阐述了这一方法如何在概率上下文中提供高效解决方案,如快速排序(QuickSort)和鸽巢原理(Pigeonhole Principle)的应用。 此外,书中还包括了大量实例、习题和案例研究,以帮助读者将理论知识转化为实际问题的解决方案。这些练习涵盖了各种难度,从基础到高级,适合不同层次的学习者。 《Algorithm Design》是学习和提升算法设计能力的宝贵资源,无论对于计算机科学的学生还是专业的软件工程师,都能从中受益匪浅。通过阅读此书,读者可以系统地学习和掌握算法设计的精髓,从而在面对复杂的计算挑战时能够游刃有余。
2018-09-19 上传
英文版 算法设计 Preface Algorithmic ideas are pervasive, and their reach is apparent in examples both within computer science and beyond. Some of the major shifts in Internet routing standards can be viewed as debates over the deficiencies of one shortest-path algorithm and the relative advantages of another. The basic notions used by biologists to express similarities among genes and genomes have algorithmic definitions. The concerns voiced by economists over the feasibility of combinatorial auctions in practice are rooted partly in the fact that these auctions contain computationally intractable search problems as special cases. And algorithmic notions aren’t just restricted to well-known and longstanding problems; one sees the reflections of these ideas on a regular basis, in novel issues arising across a wide range of areas. The scientist from Yahoo! who told us over lunch one day about their system for serving ads to users was describing a set of issues that, deep down, could be modeled as a network flow problem. So was the former student, now a management consultant working on staffing protocols for large hospitals, whom we happened to meet on a trip to New York City. The point is not simply that algorithms have many applications. The deeper issue is that the subject of algorithms is a powerful lens through which to view the field of computer science in general. Algorithmic problems form the heart of computer science, but they rarely arrive as cleanly packaged, mathematically precise questions. Rather, they tend to come bundled together with lots of messy, application-specific detail, some of it essential, some of it extraneous. As a result, the algorithmic enterprise consists of two fundamental components: the task of getting to the mathematically clean core of a problem, and then the task of identifying the appropriate algorithm design techniques, based on the structure of the problem. These two components interact: the more comfortable one is with the full array of possible design techniques, the more one starts to recognize the clean formulations that lie within messy problems out in the world. At their most effective, then, algorithmic ideas do not just provide solutions to well-posed problems; they form the language that lets you cleanly express the underlying questions. The goal of our book is to convey this approach to algorithms, as a design process that begins with problems arising across the full range of computing applications, builds on an understanding of algorithm design techniques, and results in the development of efficient solutions to these problems. We seek to explore the role of algorithmic ideas in computer science generally, and relate these ideas to the range of precisely formulated problems for which we can design and analyze algorithms. In other words, what are the underlying issues that motivate these problems, and how did we choose these particular ways of formulating them? How did we recognize which design principles were appropriate in different situations? In keeping with this, our goal is to offer advice on how to identify clean algorithmic problem formulations in complex issues from different areas of computing and, from this, how to design efficient algorithms for the resulting problems. Sophisticated algorithms are often best understood by reconstructing the sequence of ideas—including false starts and dead ends—that led from simpler initial approaches to the eventual solution. The result is a style of exposition that does not take the most direct route from problem statement to algorithm, but we feel it better reflects the way that we and our colleagues genuinely think about these questions.