C# in Depth, 3rd. Jon Skeet (PDF and Epub) For Free! Enjoy!

最经典的算法设计与分析的教材，找工作时候的必需品。

Library of Congress Cataloging-in-Publication Data Barwise, Jon. Language, proof and logic / Jon Barwise and John Etchemendy ; in collaboration with Gerard Allwein, Dave Barker-Plummer, and Albert Liu. p. cm. ISBN 1-889119-08-3 (pbk. : alk. paper)

本书以适用于逻辑第一和第二课程的方法覆盖了一阶语言，对哲学，计算机科学，数学和语言学的本科生特别有用。

Learning Go An Idiomatic Approach to Real-World Go Programming by Jon Bodner (z-lib.org).pdf

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.

飞机性能计算matlab代码乔恩·博尔曼（Jon Borman）代码示例 以下文件是在我的整个学术和职业生涯中创建的编码示例。 所有代码都是由我生成和维护的。 Convert_Files 该程序在Matlab中运行，并利用自己创建的Java存档文件将NASA数据系统（Escort）文件转换为Matlab代码。 目的是允许在部署到设施数据系统中之前轻松验证已编程的计算。 DA40_Performance_Calcs.m 这是一个简单的脚本，用于评估和可视化Diamond DA40中的升力系数与阻力系数。 该文件是在我出于对飞机性能方面的个人兴趣而放弃飞机性能课程时生成的。 Potential_Flow_Solver.m 创建此脚本是为了分配给研究生院的计算流体力学（CFD）课程。 它用于计算围绕圆柱体的不可压缩，层流和稳定流体流的二维压力场系数。 REFPROP_STPROPS.for Fortran函数是作为通用手段创建的，用于在测量下游位置计算空气特性。 该代码利用NIST REFPROP表来校正测量湿度。 VCONEFLOW.for 一个Fortran函数，用于根据ISO标准计算经

This is one of the popular text books used in universities of U.S. For example, Sudipto Guha uses the book at University of Pennsylvania. http://www.cis.upenn.edu/~cis502/ Importantly, the pdf is not a scan version, but has a REAL index. It is a high-quality one for reference.

经典算法设计教程 目录: 第1章 引言：某些典型的问题 1.1 第一个问题：稳定匹配 1.2 五个典型问题 带解答的练习 练习 注释和进一步的阅读 第2章 算法分析基础 2.1 计算可解性 2.2 增长的渐近阶 2.3 用表和数组实现稳定匹配算法 2.4 一般运行时间的概述 2.5 更复杂的数据结构：优先队列 带解答的练习 练习 注释和进一步的阅读 第3章 图 3.1 基本定义与应用 3.2 图的连通性与图的遍历 3.3 用优先队列与栈实现图的遍历 3.4 二分性测试：宽度优先搜索的一个应用 3.5 有向图中的连通性 3.6 有向无圈图与拓扑排序 带解答的练习 练习 注释和进一步的阅读 第4章 贪心算法 4.1 区间调度：贪心算法领先 4.2 最小延迟调度：一个交换论证 4.3 最优高速缓存：一个更复杂的交换论证 4.4 一个图的最短路径 4.5 最小生成树问题 4.6 实现Kruskal算法：Unoin－Find数据结构 4.7 聚类 4.8 Huffman码与数据压缩 4.9 最小费用有向树：一个多阶段贪心 带解答的练习 练习 注释和进一步的阅读 第5章 分治策略 5.1 第一个递推式：归并排序算法 5.2 更多的递推关系 5.3 计数逆序 5.4 找最接邻近的点对 5.5 整数乘法 5.6 卷积与快速傅里叶变换 带解答的练习 练习 注释和进一步的阅读 第6章 动态规划 6.1 带权的区间调度：一个递归过程 6.2 动态规划原理：备忘录或者子问题迭代 6.3 分段的最小二乘：多重选择 6.4 子集和与背包：加一个变量 6.5 RNA二级结构：在区间上的动态规划 6.6 序列比对 6.7 通过分治策略在线性空间的序列比对 6.8 图中的最短路径 6.9 最短路径和距离向量协议 6.10 图中的负圈 带解答的练习 练习 注释和进一步的阅读 第7章 网络流 第8章 Ng与计算的难解性 第9章 一个超出 第10章 扩展易解性的界限 第11章 近似算法 第12章 局部搜索 第13章 随机算法 后记：永不停止运行的算法 索引

Algorithm Design by Jon Kleinberg, Eva Tardos.pdf This is a fundamental book for learning introduction and advanced algorithms.