算法设计与分析书后习题答案解析

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《算法设计与分析》是一本深入探讨算法理论与实践的经典教材,第二版由清华大学出版社出版,包含了丰富的习题和解答,旨在帮助读者理解和掌握算法设计的基本原理与技巧。书后的参考答案对学习者理解和消化课程内容起到了关键作用。 第一章涵盖了选择题、填空题和简答题,涉及了核心概念如算法的特性(如确定性、可行性、有穷性等)、程序设计语言的高级性(高级语言的易用性、结构化编程、可移植性和自动化等)、抽象数据类型的重要性(降低复杂性、增强条理、提高代码质量、可维护性、模块化和算法证明等)。算法复杂度是衡量算法效率的重要指标,包括时间复杂度和空间复杂度,而渐进复杂度则是在问题规模增长时算法性能的长期行为。 在填空题中,学生被要求填写诸如“基本运算”、“精确算法与启发式算法的区别”以及“算法复杂度的优化目标”等关键概念,这些内容强调了基础理论的理解与应用。例如,正确性、简明性、高效性和最优性是评价算法质量的重要维度。 简答题部分深入解析了高级语言的优势和抽象数据类型在算法设计中的作用,阐述了如何通过模块化和抽象降低设计难度,提高程序的可读性和可维护性。此外,还讨论了算法复杂度的概念以及其在分析和优化算法性能中的重要性。 在处理问题规模变化时,理解并分析算法的渐进复杂度对于设计和优化高效算法至关重要。书中可能还涉及到了复杂度分析的不同状态(最好性态、最坏性态和平均性态),以及如何在实际问题中权衡时间和空间资源。 《算法设计与分析》书后的参考答案为学习者提供了理论与实践相结合的学习材料,帮助他们掌握算法设计的关键技能,并培养解决实际问题的能力。通过解答中对复杂概念的详尽解释,读者不仅能够解答题目,还能深化对算法本质的理解。
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.