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.

《计算复杂性的现代方法》是一部将所有有关复杂度知识理论集于一体的教程。将最新进展和经典结果结合起来，是一部很难得的研究生入门级教程。既是相关科研人员的一部很好的参考书，也是自学人员很难得的一本很好自学教程。本书一开始引入该领域的最基本知识，然后逐步深入，介绍更多深层次的结果，每章末都附有练习。对复杂度感兴趣的人士，物理学家，数学家以及科研人员这本书都是相当受益。

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

第1卷首先介绍编程的基本概念和技术，然后详细讲解信息结构方面的内容，包括信息在计算机内部的表示方法、数据元素之间的结构关系，以及有效的信息处理方法。此外，书中还描述了编程在模拟、数值方法、符号计算、软件与系统设计等方面的初级应用。此第3版增加了数十项简单但重要的算法和技术，并根据当前研究发展趋势在数学预备知识方面做了大量修改。

本书是计算机类专业操作系统课程的一本经典教材，自第一版问世以来，经历了近20年的锤炼，被认为是该课程教材的一本圣经。它对操作系统的概念和基本原理给出了清晰的阐述。本书所涉及的基本概念和算法均基于当前商用操作系统，并在非特定操作系统的通用环境中展开讲解。书中介绍了大量与流行操作系统相关的实现技术，包括Solaris 2、Linux、Windows NT、Windows 2000、OS/2和Apple Macintosh操作系统。此版包括了线程、Windows 2000的新章节，并新增了客户/服务器模型和网络文件系统、嵌入式操作系统、实时操作系统、分布式操作系统等。
作者Abraham Silerschatz是贝尔实验室信息科学研究中心的副主任， Greg Gagne是威斯敏斯特学院计算机学系主任，Peter Baer Galvin曾在布朗大学计算机科学系执教，现为Corporate Technologies公司的首席技术专家。

This volume contains a collection of clever mathematical applications of linear algebra, mainly in combinatorics, geometry, and algorithms. Each chapter covers a single main result with motivation and full proof in at most ten pages and can be read independently of all other chapters (with minor exceptions), assuming only a modest background in linear algebra. The topics include a number of well-known mathematical gems, such as Hamming codes, the matrix-tree theorem, the Lovasz bound on the Shannon capacity, and a counterexample to Borsuk's conjecture, as well as other, perhaps less popular but similarly beautiful results, e.g., fast associativity testing, a lemma of Steinitz on ordering vectors, a monotonicity result for integer partitions, or a bound for set pairs via exterior products. The simpler results in the first part of the book provide ample material to liven up an undergraduate course of linear algebra. The more advanced parts can be used for a graduate course of linear-algebraic methods or for seminar presentations. Table of Contents: Fibonacci numbers, quickly; Fibonacci numbers, the formula; The clubs of Oddtown; Same-size intersections; Error-correcting codes; Odd distances; Are these distances Euclidean?; Packing complete bipartite graphs; Equiangular lines; Where is the triangle?; Checking matrix multiplication; Tiling a rectangle by squares; Three Petersens are not enough; Petersen, Hoffman-Singleton, and maybe 57; Only two distances; Covering a cube minus one vertex; Medium-size intersection is hard to avoid; On the difficulty of reducing the diameter; The end of the small coins; Walking in the yard; Counting spanning trees; In how many ways can a man tile a board?; More bricks--more walls?; Perfect matchings and determinants; Turning a ladder over a finite field; Counting compositions; Is it associative?; The secret agent and umbrella; Shannon capacity of the union: a tale of two fields; Equilateral sets; Cutting cheaply using eigenvectors; Rotating the cube; Set pairs and exterior products; Index. (STML/53)

本书是一本在大学中广泛使用的经典数学教科书。书中讲解了许多计算机科学中用到的数学知识和技巧，教你如何把一个实际问题一步步演化为数学模型，然后通过计算机解决它，特别着墨于算法分析方面，其主要内容涉及和式、整值函数、数论、二项式系数、特殊的数、生成函数、离散概率、渐近式等，都是编程所必备的知识。
书中不仅讲述了数学问题和技巧，而且教导解决问题的方法，解说深入浅出，妙趣横生。大师们诙谐、细腻的笔触，描绘着数学工作中的欢乐和忧伤，那些或平淡、或深刻、或严肃、或幽默的涂鸦，更让我们在轻松愉悦的心境下体会数学的美妙。
本书面向从事计算机科学、计算数学、计算技术诸方面工作的人员，以及高等院校相关专业的师生。

大数据是当前最为流行的热点概念之一，其已由技术名词衍生到对很多行业产生颠覆性影响的社会现象，作为最明确的技术发展趋势之一，基于大数据的各种新型产品必将会对每个人的日常生活产生日益重要的影响。
《大数据日知录：架构与算法》从架构与算法角度全面梳理了大数据存储与处理的相关技术。大数据技术具有涉及的知识点异常众多且正处于快速演进发展过程中等特点，其技术点包括底层的硬件体系结构、相关的基础理论、大规模数据存储系统、分布式架构设计、各种不同应用场景下的差异化系统设计思路、机器学习与数据挖掘并行算法以及层出不穷的新架构、新系统等。《大数据日知录：架构与算法》对众多纷繁芜杂的相关技术文献和系统进行了择优汰劣并系统性地对相关知识分门别类地进行整理和介绍，将大数据相关技术分为大数据基础理论、大数据系统体系结构、大数据存储，以及包含批处理、流式计算、交互式数据分析、图数据库、并行机器学习的架构与算法以及增量计算等技术分支在内的大数据处理等几个大的方向。通过这种体系化的知识梳理与讲解，相信对于读者整体和系统地了解、吸收和掌握相关的优秀技术有极大的帮助与促进作用。
《大数据日知录：架构与算法》的读者对象包括对NoSQL 系统及大数据处理感兴趣的所有技术人员，以及有志于投身到大数据处理方向从事架构师、算法工程师、数据科学家等相关职业的在校本科生及研究生。

本书是国际上数值计算方面的权威著作，有“圣经”之称。被美国加州大学、斯坦福大学、华盛顿大学、芝加哥大学、中国科学院研究生院等很多世界知名学府用作相关课程的教材或主要参考书。
本书系统地介绍了矩阵计算的基本理论和方法。书中的许多算法都有现成的软件包实现，每节后还附有习题，并有注释和大量参考文献，非常有助于自学。

Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates.

Essential Information about Algorithms and Data Structures A Classic Reference The latest version of Sedgewick,s best-selling series, reflecting an indispensable body of knowledge developed over the past several decades. Broad Coverage Full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing, including fifty algorithms every programmer should know. See

《支持向量机：理论、算法与拓展》以分类问题（模式识别、判别分析）和回归问题为背景，介绍支持向量机的基本理论、方法和应用。特别强调对所讨论的问题和处理方法的实质进行直观的解释和说明，因此具有很强的可读性。为使具有一般高等数学知识的读者能够顺利阅读，书中首先介绍了最优化的基础知识。《支持向量机：理论、算法与拓展》可作为理工类、管理学等专业的高年级本科生、研究生和教师的教材或教学参考书，也可供相关领域的科研人员和实际工作者阅读参考。

《组合数学》(原书第4版)侧重于组合数学的概念和思想，包括鸽巢原理、计数技术、排列组合、Polya计数法、二项式系数、容斥原理、生成函数和递推关系以及组合结构(匹配、实验设计、图)等，深入浅出地表达了作者对该领域全面和深刻的理解，介绍了历史上源于数学游戏和娱乐的大量实例，其中对Polya计数、Burnside定理等的完美处理使得不熟悉群论的学生也能够读懂。除包含第3版中的内容外，本版又进行了更新，增加了莫比乌斯反演(作为容斥原理的推广)、格路径、Schroder数等内容。此外，各章均包含大量练习题，并在书末给出了参考答案与提示。

《组合数学(英文版)(第5版)》英文影印版由Pearson Education Asia Ltd．授权机械工业出版社独家出版。未经出版者书面许可，不得以任何方式复制或抄袭奉巾内容。仅限于中华人民共和国境内(不包括中国香港、澳门特别行政区和中同台湾地区)销售发行。《组合数学(英文版)(第5版)》封面贴有Pearson Education(培生教育出版集团)激光防伪标签，无标签者不得销售。English reprint edition copyright@2009 by Pearson Education Asia Limited and China Machine Press．
Original English language title：Introductory Combinatorics，Fifth Edition(ISBN978—0—1 3-602040-0)by Richard A．Brualdi，Copyright@2010，2004，1999，1992，1977 by Pearson Education，lnc． All rights reserved．
Published by arrangement with the original publisher，Pearson Education，Inc．publishing as Prentice Hall．
For sale and distribution in the People’S Republic of China exclusively(except Taiwan，Hung Kong SAR and Macau SAR)．

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.

《马尔可夫链：模型、算法与应用 应用数学译丛》讲述了马尔可夫链模型在排队系统、网页重要性排名、制造系统、再制造系统、库存系统以及金融风险管理等方面的最新应用进展.全书共安排8章内容，第1章介绍马尔可夫链、隐马尔可夫模型和马尔可夫决策过程的基本理论和方法，其余7章分别介绍马尔可夫链模型在不同领域中的应用. 《马尔可夫链：模型、算法与应用 应用数学译丛》可作为自动化、工业工程、统计学、应用数学以及管理学等专业高年级本科生或研究生的专业课教材，也可作为相关领域的研究人员及工程技术人员的参考书.

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

《人工智能:一种现代方法》(第2版中文版)以详尽和丰富的资料，从理性智能体的角度，全面阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向，是一本难得的综合性教材。全书分为八大部分：第一部分“人工智能” ，第二部分“问题求解” ，第三部分“ 知识与推理” ，第四部分“规划” ，第五部分“不确定知识与推理” ，第六部分“学习” ，第七部分“通讯、感知与行动” ，第八部分“ 结论” 。