It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades. "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.

《走向数学丛书03-数学模型选谈》，本书主要介绍了关于在等高线图上计算矿藏储量与坡地面积的问题、挂轮问题、挂轮问题、优选法(单、多因素)、黄金数与数值积分和统筹方法等数学模型问题。

For undergraduate or graduate courses in Graph Theory in departments of mathematics or computer science. This text offers a comprehensive and coherent introduction to the fundamental topics of graph theory. It includes basic algorithms and emphasizes the understanding and writing of proofs about graphs. Thought-provoking examples and exercises develop a thorough understanding of the structure of graphs and the techniques used to analyze problems. The first seven chapters form the basic course, with advanced material in Chapter 8.

本书编写了300多个实用而有效的数值算法C语言程序。其内容包括：线性方程组的求解，逆矩阵和行列式计算，多项式和有理函数的内插与外推，函数的积分和估值，特殊函数的数值计算，随机数的产生，非线性方程求解，傅里叶变换和FFT，谱分析和小波变换，统计描述和数据建模，常微分方程和偏微分方程求解，线性预测和线性预测编码，数字滤波，格雷码和算术码等。全书内容丰富，层次分明，是一本不可多得的有关数值计算的C语言程序大全。本书每章中都论述了有关专题的数学分析、算法的讨论与比较，以及算法实施的技巧，并给出了标准C语言实用程序。这些程序可在不同计算机的C语言编程环境下运行。 本书可作为从事科学计算的科技工作者的工具书，计算机软件开发者的参考书，也可以作为大学本科生和研究生的参考书或教材。

《MATLAB在数学建模中的应用》从数学建模的角度介绍了MATLAB的应用。《MATLAB在数学建模中的应用》的4位作者均具有实际的数学建模参赛经历和竞赛指导经验。书中内容完全是根据数学建模竞赛的需要而编排的，涵盖了绝大部分数学建模问题的MATLAB求解方法。 《MATLAB在数学建模中的应用》内容分上下两篇。上篇介绍数学建模中常规方法MATLAB的实现，包括MATLAB交互、数据建模、程序绘图、灰色预测、规划模型等方法；还介绍了各种高级方法MATLAB的实现，包括遗传算法、粒子群算法、模拟退火算法、人工神经网络、小波分析、动态仿真、数值模拟等。下篇以真实的数学建模赛题为案例，介绍了如何用MATLAB求解实际的数学建模问题，给出了详细的建模过程和程序。书中的附件部分介绍了作者在建模竞赛中屡获大奖的经验。相信这些经验对准备参加数学建模竞赛的读者会有所帮助。 《MATLAB在数学建模中的应用》特别适合作为数学建模竞赛的培训教材或参考用书，也可作为大学“数学实验”和“数学建模”以及“数据挖掘”课程的参考用书，还可以作为广大科研人员、学者、工程技术人员的参考用书。

Algorithmic puzzles are puzzles involving well-defined procedures for solving problems. This book will provide an enjoyable and accessible introduction to algorithmic puzzles that will develop the reader's algorithmic thinking. The first part of this book is a tutorial on algorithm design strategies and analysis techniques. Algorithm design strategies - exhaustive search, backtracking, divide-and-conquer and a few others - are general approaches to designing step-by-step instructions for solving problems. Analysis techniques are methods for investigating such procedures to answer questions about the ultimate result of the procedure or how many steps are executed before the procedure stops. The discussion is an elementary level, with puzzle examples, and requires neither programming nor mathematics beyond a secondary school level. Thus, the tutorial provides a gentle and entertaining introduction to main ideas in high-level algorithmic problem solving. The second and main part of the book contains 150 puzzles, from centuries-old classics to newcomers often asked during job interviews at computing, engineering, and financial companies. The puzzles are divided into three groups by their difficulty levels. The first fifty puzzles in the Easier Puzzles section require only middle school mathematics. The sixty puzzle of average difficulty and forty harder puzzles require just high school mathematics plus a few topics such as binary numbers and simple recurrences, which are reviewed in the tutorial. All the puzzles are provided with hints, detailed solutions, and brief comments. The comments deal with the puzzle origins and design or analysis techniques used in the solution. The book should be of interest to puzzle lovers, students and teachers of algorithm courses, and persons expecting to be given puzzles during job interviews.

Discrete Mathematics and its Applications is a focused introduction to the primary themes in a discrete mathematics course, as introduced through extensive applications, expansive discussion, and detailed exercise sets. These themes include mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and enhanced problem-solving skills through modeling. Its intent is to demonstrate the relevance and practicality of discrete mathematics to all students. The Fifth Edition includes a more thorough and linear presentation of logic, proof types and proof writing, and mathematical reasoning. This enhanced coverage will provide students with a solid understanding of the material as it relates to their immediate field of study and other relevant subjects. The inclusion of applications and examples to key topics has been significantly addressed to add clarity to every subject. True to the Fourth Edition, the text-specific web site supplements the subject matter in meaningful ways, offering additional material for students and instructors. Discrete math is an active subject with new discoveries made every year. The continual growth and updates to the web site reflect the active nature of the topics being discussed. The book is appropriate for a one- or two-term introductory discrete mathematics course to be taken by students in a wide variety of majors, including computer science, mathematics, and engineering. College Algebra is the only explicit prerequisite.

《计算几何:算法与应用》(第2版)的前4章对几何算法进行了讨论，包括几何求交、三角剖分、线性规划等，其中涉及的随机算法也是《计算几何:算法与应用》(第2版)的一个鲜明特点。第5章至第10章介绍了多种几何结构，包括几何查找、kd树、区域树、梯形图、Voronoi图、排列、Delaunay三角剖分、区间树、优先查找树以及线段树等。第11章至第16章结合实际问题，继续讨论了若干几何算法及其数据结构，包括高维凸包、空间二分及BSP树、运动规划、网格生成及四叉树、最短路径查找及可见性图、单纯性区域查找及划分树和切分树等，这些也是对前十章内容的进一步深化。

Clearly written graduate-level text considers the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; approximation algorithms, local search heuristics for NP-complete problems, more. "Mathematicians wishing a self-contained introduction need look no further." -- "American Mathematical Monthly." 1982 edition..

本书系统地介绍了与程序设计竞赛有关的组合数学的基本理论和算法设计与分析的常用方法。全书共分8章，分别为：算法基础、组合数学初探、排列与组合、容斥原理、母函数、拟阵、贪心算法和Pólya定理。本书突出组合数学算法的设计与优化，从而更便于参加程序设计竞赛的读者学习组合数学。 本书可作为ACM/ICPC国际大学生程序设计竞赛和国际信息学奥林匹在竞赛（IOI）的培训教材，也可供从事组合数学与算法研究的人员参考。

The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

【编辑推荐】 由在IBM工作50余年的资深计算机专家撰写，Amazon全五星评价，算法领域最有影响力的著作之一 Google公司首席架构师、Jolt大奖得主Hoshua Bloch和Emacs合作创始人、C语言畅销书作者Guy Steele倾情推荐 算法的艺术和数学的智慧在本书中得到了完美体现，书中总结了大量高效、优雅和奇妙的算法，并从数学角度剖析了其背后的原理 【读者评价】 “这是第一本宣称能讲解计算机算法隐晦细节的书，而且讲得还真不错。我知道的每一条技巧书里都提到了，而且还讲了好多好多我不知道的。不论是在开发程序库或编译器，还是在极力搜求优雅算法，此书都可谓天赐良册，应放在高德纳所著《计算机程序设计艺术》那套书旁边。本书第一版刊印后的10年间，它对我在Sun和Google的工作大有裨益，而第二版所添加新内容亦令我惊羡不已。” —— Joshua Bloch “初看本书书名时，我想，这是教人怎么入侵计算机系统的书吗？不太可能吧。嗯，那就肯定是一本编程小技巧的集锦。看了之后发现，没错，这就是一本编程秘籍，然而却是一本包罗万象的秘籍。第二版新增了两个大主题，并用数十个小技巧丰富了本书内容，其中有个小绝招是如何在不溢出的情况下求两数均值，我写二分查找算法时直接就把这条拿来用了。这真是本令算法爱好者开怀畅读的书啊！” —— Guy Steele 【内容简介】 在本书中，作者给我们带来了一大批极为诱人的知识，其中包括各种节省程序运行时间的技巧、算法与窍门。学习了这些技术，程序员就可写出优雅高效的软件，同时还能洞悉其中原理。这些技术极为实用，而且其问题本身又非常有趣，有时甚至像猜谜解谜一般，需要奇思妙想才行。简而言之，软件开发者看到这些改进程序效率的妙计之后，定然大喜。 本书较第1版增补了大量内容 新增了循环冗余校验（CRC）一章，其中讲解了常用的CRC-32校验码 新增了纠错码（ECC）一章，其中讲解了汉明码 详解了除数为常数的整数除法，增补了仅含移位操作和加法操作的算法 不计算商而直接求余数 扩充了与种群计数和前导0计数有关的知识 数组种群计数 执行压缩与扩展操作的新算法 LRU算法 浮点数与整数互化 估算浮点数的平方根倒数 一系列离散函数图像 各章均配有习题与参考答案

Assuming only an elementary background in discrete mathematics, this textbook is an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It includes random sampling, expectations, Markov's and Chevyshev's inequalities, Chernoff bounds, balls and bins models, the probabilistic method, Markov chains, MCMC, martingales, entropy, and other topics. The book is designed to accompany a one- or two-semester course for graduate students in computer science and applied mathematics.

本书介绍组合数学中的基础理论和实际应用，讲述的内容非常广泛，讨论的问题涵盖组合数学所涉及的绝大部分领域。本书不仅包含了通常组合数学教科书中的经典内容，而且收集了若干新的内容，如Lovász筛法、范德瓦尔登积和式猜想、结合区组设计、码和设计等。 本书阐述深入浅出，简明易懂，适合作为高等院校高年级本科生与低年级研究生的组合数学课程教材，也适合作为数学和其他学科的研究人员的参考书。

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.Drawing on their experiences in teaching, research, and consulting, the authors have produced a textbook that will be of interest to students and practitioners alike. Each chapter begins with the basic concepts and builds up gradually to the best techniques currently available.Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field.Above all, the authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.MMOR Mathematical Methods of Operations Research, 2001: "The book looks very suitable to be used in an graduate-level course in optimization for students in mathematics, operations research, engineering, and others. Moreover, it seems to be very helpful to do some self-studies in optimization, to complete own knowledge and can be a source of new ideas.... I recommend this excellent book to everyone who is interested in optimization problems."

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.

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.

Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in colour for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior point methods for linear programming; MCMC; an expanded treatment of ODEs with completely new routines; and many new statistical distributions. For support, or to subscribe to an online version, please visit www.nr.com. • Most comprehensive book available on scientific computing, now updated • New routines for classification and inference HMMs and SVMs, computational geometry, ODEs, interior point methods for linear programming, and MCMC • Over 600,000 Numerical Recipes products in print Contents 1. Preliminaries; 2. Solution of linear algebraic equations; 3. Interpolation and extrapolation; 4. Integration of functions; 5. Evaluation of functions; 6. Special functions; 7. Random numbers; 8. Sorting and selection; 9. Root finding and nonlinear sets of equations; 10. Minimization or maximization of functions; 11. Eigensystems; 12. Fast Fourier transform; 13. Fourier and spectral applications; 14. Statistical description of data; 15. Modeling of data; 16. Classification and inference; 17. Integration of ordinary differential equations; 18. Two point boundary value problems; 19. Integral equations and inverse theory; 20. Partial differential equations; 21. Computational geometry; 22. Less-numerical algorithms; References.

本书是一本专门为程序员而写的数学书，介绍了程序设计中常用的数学知识。本书门槛不高，不需要读者精通很多高深的数学知识，只需要读者具备基本的四则运算、乘方等数学基础知识和日常生活中的基本逻辑判断能力即可。本书拒绝枯燥乏味的讲解，而是代之以轻松活泼的风格。书中列举了大量读者都很熟悉，而且非常有趣的数学实例，并结合程序设计的思维和算法加以剖析，可以训练读者的数学思维能力和程序设计能力，进而拓宽读者的视野，增强职场竞争力。 本书共11章，分别介绍了数据的表示、神奇的素数、递归、排列组合、用余数进行数据分组、概率、复利、数理逻辑、推理、几何图形构造、统筹规划等程序设计中常用的数学知识，从而引导读者深入理解编程中的数学方法和思路。本书包含的实例有结绳记事、孪生素数、梅森素数、哥德巴赫猜想、阶乘、汉诺塔、斐波那契数列、乘法原理、加法原理、字符编码、密码长度、日历中的数学、心灵感应魔术、约瑟夫环、智叟分牛、百枚钱币鼓士气、庄家的胜率、中奖概率、用概率方法求π值、复利的威力、对折纸张、舍罕王的赏赐、三段论、选言推理、假言推理、关系推理、花盆摆放、残缺棋盘、丢失的线条、田忌赛马、背包问题等。 本书适合广大程序设计人员及数学爱好者阅读，尤其适合有一定程序设计经验，但还需要进一步加深对程序设计理解的人员阅读。本书对IT求职人员、信息学竞赛和大学生程序设计竞赛等参赛学员也有很好的参考价值。

本书是计算理论领域的经典著作，被国外多所大学选用为教材。本书以注重思路、深入引导为特色，系统地介绍计算理论的三大主要内容：自动机与语言、可计算性理论和计算复杂性理论。同时，对可计算性和计算复杂性理论中的某些高级内容作了重点讲解。全书通过启发性的问题、精彩的结果和待解决问题来引导读者挑战此领域中的高层次问题。新版的一大亮点是增加了更多习题、教辅资料和部分习题解答，更加有利于教学。 全书叙述由浅入深、详略得当，重点突出，不拘泥于技术细节。可作为计算机专业高年级本科生和研究生的教材，也可作为相关专业教师和研究人员的参考书。