《算法设计手册(第2版)》是算法设计畅销书的最新版本，是设计实用且高效算法的最全面指导书。《算法设计手册(第2版)》揭密了算法的设计与分析，以简单易懂的写作风格，介绍了各种算法技术，着重强调了算法分析，全书包括两大部分，“技术”部分介绍了设计和分析计算机算法的各种方法，“资源”部分给出了大量的参考资源，以及算法实现的各种资源，此外，在作者的个人网址http：／／www．CS．sunysb．edu／～algorith／I-还提供了各种教学资源和参考材料，这些资源对读者很有参考价值。
《算法设计手册(第2版)》可以作为算法设计课程的主教材，也是程序人员、研究人员和学生的常备参考书。

In "Distributed Algorithms", Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers. "Distributed Algorithms" contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others. The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference. The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms - to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.

'This book covers the dominant theoretical approaches to the approximate solution of hard combinatorial optimization and enumeration problems. It contains elegant combinatorial theory, useful and interesting algorithms, and deep results about the intrinsic complexity of combinatorial problems. Its clarity of exposition and excellent selection of exercises will make it accessible and appealing to all those with a taste for mathematics and algorithms' - Richard Karp, University Professor, University of California at Berkeley. Following the development of basic combinatorial optimization techniques in the 1960s and 1970s, a main open question was to develop a theory of approximation algorithms. In the 1990s, parallel developments in techniques for designing approximation algorithms as well as methods for proving hardness of approximation results have led to a beautiful theory. The need to solve truly large instances of computationally hard problems, such as those arising from the Internet or the human genome project, has also increased interest in this theory. The field is currently very active, with the toolbox of approximation algorithm design techniques getting always richer. It is a pleasure to recommend Vijay Vazirani's well-written and comprehensive book on this important and timely topic. "I am sure the reader will find it most useful both as an introduction to approximability as well as a reference to the many aspects of approximation algorithms' - Laszlo Lovasz, Senior Researcher, Microsoft Research.

本书是国际算法大师乌迪·曼博（Udi Manber）博士撰写的一本享有盛誉的著作。全书共分12章：第1章到第4章为介绍性内容，涉及数学归纳法、算法分析、数据结构等内容；第5章提出了与归纳证明进行类比的算法设计思想；第6章到第9章分别给出了4个领域的算法，如序列和集合的算法、图算法、几何算法、代数和数值算法；第10章涉及归约，也是第11章的序幕，而后者涉及NP完全问题；第12章则介绍了并行算法；最后是部分习题的答案及参考文献。本书的特色有二，旨在提高读者的问题求解能力，使读者能够理解算法设计的过程和思想：一是强调算法设计的创造性过程，注重算法设计背后的创造性思想，而不拘泥于某个具体算法的详细讨论；二是将算法设计类比于定理归纳证明，揭示了算法设计的基本思想和本质。
本书的组织结构清晰且易于理解，强调了创造性，具有浓郁特色，时至今日仍有其巨大的价值，并且适合作为计算机及相关专业算法和高级算法课程的教材。

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

This well-accepted introduction to computational geometry is a textbook for high-level undergraduate and low-level graduate courses. The focus is on algorithms and hence the book is well suited for students in computer science and engineering. Motivation is provided from the application areas: all solutions and techniques from computational geometry are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. For students this motivation will be especially welcome. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement. All the basic techniques and topics from computational geometry, as well as several more advanced topics, are covered. The book is largely self-contained and can be used for self-study by anyone with a basic background in algorithms. In this third edition, besides revisions to the second edition, new sections discussing Voronoi diagrams of line segments, farthest-point Voronoi diagrams, and realistic input models have been added.

This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal.

For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.

计算机程序设计艺术：英文版（第2卷 半数值算法），ISBN：9787302058151，作者：（美）Donald E. Knuth著

This course is offered to undergraduates and is an elementary discrete mathematics course oriented towards applications in computer science and engineering. Topics covered include: formal logic notation, induction, sets and relations, permutations and combinations, counting principles, and discrete probability.

这是一本关于生物信息学算法和计算思想的导论性教科书，原著由国际上的权威学者撰写，经国内知名专家精心翻译为中文，系统介绍推动生物信息学不断进步的算法原理。全书强调的是算法中思想的运用，而不是对表面上并不相关的各类问题进行简单的堆砌。 体现了以下特色： 阐述生物学中的相关问题，涉及对问题的模型化处理并提供一种或多种解决方案： 简要介绍生物信息学领域领军人物； 饶有趣味的小插图使得概念更加具体和形象，方法更容易被领会，激励学生学习的兴趣并鼓励他们加入到生物信息学研究工作中来。 书中的大量论述表明：较少的几种设计思想就能解决大量的生物学难题。 目前，可供本科学生使用的生物信息学著作为数不多，本书恰恰是其中的一本。国内生物信息学，计算生物学、计算数学等领域的本科生、研究生和其他研究人员，会从书中汲取基本的算法原理、解决实际问题的方法和技巧，进而更好地从事相关研究工作。

This book introduces the mathematics that supports advanced computer Programming and the analysis of algorithms. The primary aim of its well-known authors is to provide a solid and relevant base of mathematical skills--the skills needed to solve complex problems, to evaluate horrendous sums, and to discover subtle Patterns in data. It is an indispensable text and reference not only for computer scientists--the authors themselves rely heavily on it! but for serious users Of mathematics in virtually every discipline. Concrete mathematics is a blending of continuous and disCRETE mathematics: "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas,using a collection of techniques for solving problems." The subject mater is primarily an expansion of the Mathematical Preliminaries section in Knuth's c1assic Art of Computer Programming, but the style of presentation is more leisurely, and individual topics are covered more deeply. Several new topics have been added, and the most significant ideas have been traced to their historical roots. The book includes more than 500 exercises, divided into six categories. Complete answers are provided for all exercises, except research problems, making the book particularly valuable for self-study.