“数据结构”是计算机程序设计的重要理论技术基础，它不仅是计算机学科的核心课程，而且已成为其他理工专业的热门选修课。本书是为“数据结构”课程编写的教材，其内容选取符合教学大纲要求，并兼顾学科的广度和深度，适用面广。 　　本书可作为计算机类专业的本科或专科教材，也可以作为信息类相关专业的选修教材，讲授学时可为50至80。教师可根据学时、专业和学生的实际情况，选讲或不讲目录页中带**的章节，甚至删去第5，8，11和12章。本书文字通俗、简明易懂、便于自学，也可供从事计算机应用等工作的科技人员参考。只需掌握程序设计基本技术便可学习本书。若具有离散数学和概率论的知识，则对书中某些内容更易理解。如果将本书《数据结构》（C语言版）和《数据结构》（第二版）作为关于数据结构及其箩法的C和Pascal程序设计的对照教材，则有助于快速且深刻地掌握这两种语言。 　　《数据结构》（C语言版）是为“数据结构”课程编写的教材，也可作为学习数据结构及其算法的C程序设计的参考教材。 　　本书的前半部分从抽象数据类型的角度讨论各种基本类型的数据结构及其应用；后半部分主要讨论查找和排序的各种实现方法及其综合分析比较。其内容和章节编排与1992年4月出版的《数据结构》（第二版）基本一致，但在本书中更突出了抽象数据类型的概念。全书采用类C语言作为数据结构和算法的描述语言。 　　本书概念表述严谨，逻辑推理严密，语言精炼，用词达意。并有配套出版的《数据结构题集）（C语言版）。既便于教学，又便于自学。 　　本书后附有光盘，光盘中含有可在DOS环境下运行的以类C语言描述的“数据结构算法动态模拟辅助教学软件，以及在Windows环境下运行的以类PASCAL或类C两种语言描述的“数据结构算法动态模拟辅助教学软件”。 　　本书可作为计算机类专业或信息类相关专业的本科或专科教材，也可供从事计算机工程与应用工作的科技工作者参考。

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。

《Flash ActionScript 3.0 动画高级教程》是介绍Flash 10 ActionScript动画高级技术的经典之作，是作者在这一领域中多年实践经验的结晶。书中不仅涵盖了3D、最新绘图API以及Pixel Bender等Flash 10 ActionScript特性，深入介绍了碰撞检测、转向、寻路等Flash游戏开发技术，还通过实例具体讲解了等角投影和数值积分的基本理论和应用。 《Flash ActionScript 3.0 动画高级教程》内容紧扣实际应用，适合各层次Flash开发人员阅读。

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. The book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software. This is not an easy book. Nor is it a compilation of tips and tricks for incremental improvements in your programming skills. The book’s value is more fundamental and, ultimately, more critical for insight into programming. To benefit fully, you will need to work through it from beginning to end, reading the code, proving the lemmas, doing the exercises. When finished, you will see how the application of the deductive method to your programs assures that your system’s software components will work together and behave as they must. Following key definitions, the book describes a number of algorithms and requirements for types on which they are defined that exemplify its abstract mathematical approach. The code for these descriptions—also available on the Web—is written in a small subset of C++ meant to be accessible to any experienced programmer. This subset is defined in a special language appendix coauthored by Sean Parent and Bjarne Stroustrup. Whether you are a software developer, or any other professional for whom programming is an important activity, or a committed student, you will come to understand what the book’s experienced authors have been teaching and demonstrating for years—that mathematics is good for programming, that theory is good for practice.

《应用组合数学(第5版)》讲解了离散数学问题求解中组合推理和组合建模的方法、思维和运用。主要涉及图论基本概念、覆盖和图着色、搜索算法和网络运算算法等图论知识和方法，以及基本的计数方法、生成函数计数模型、递推关系模型、容斥原理、Polya枚举公式等枚举方法及其应用。作者还介绍了如何用计算机科学地处理枚举，以及逐步受限游戏的理论及其在尼姆游戏中的应用，体现了组合数学的趣味性。 《应用组合数学(第5版)》内容丰富，简明易懂，适合作为高等院校数学专业和计算机专业高年级本科生及研究生的教材，也可供对组合数学有兴趣的相关人员阅读。