具体描述
内容简介
This is the second volume of the textbook "Fundamentals of Advanced Math-ematics" written by the same authors. It includes vector algebra and analytic geometry in space, multivariable calculus, and linear ordinary differential e-quations. The intentions and features are as introduced in the preface to the first volume. We repeat here the important advice to students in the first vol-ume, as it is equally important for this second volume.In order to learn calculus, it is not enough to read the textbook as if it were a newspaper. Learning requires careful reading, working through exam-ples step by step, and solving problems. Solving problems requires more than imitation of examples. It is necessary to think about what the problem really asks and to develop a method for that particular problem.If something is still not clear after you have tried to understand it, you should ask a classmate, a more advanced student, or your teacher. If a classmate asks you a question, you may learn a great deal from explaining the answer.
The following two additional remarks might be helpful to readers in u-sing the second volume.
(1) The material on linear systems of ordinary differential equations (Section 9.2) is not included in the fundamental requirements. Before study-ing it, readers will need some basic knowledge of linear algebra.
(2) Some of the material in this volume has been stated in terms of ma-trices and determinants. For readers who are not yet familiar with the basic concepts and operations for matrices and determinants we have included a brief outline in Appendix A. 目录
Chapter 5 Vector Algebra and Analytic Geometry in Space
5.1 Vectors and Their Linear Operations
5.1.1 The concept of vector
5.1.2 Linear operationS on vectorS
5.1.3 Projection of vectors
5.1.4 Rectangular coordinate systems in space and components of Vectors
Exercises 5.1
5.2 Multiplicative Operations on Vectors
5.2.1 The scalar product(dot product,inner product)of two vectorS
5.2.2 The vector product(cross product,outer product)of two vectors
5.2.3 The mixed product of three vectors
Exercises 5.2
5.3 Planes and Lines in Space
5.3.1 Equations of planes
5.3.2 Position relationships between two planes
5.3.3 Equations of straight lines in space
5.3.4 Position relationships between two lines
5.3.5 Position relationships between a line and a plane
5.3.6 Distance from a point to a plane(1ine)
Exercises 5.3
5.4 Surfaces and Space Curves
5.4.1 Equations of surfaces
5.4.2 Quadric surfaces
5.4.3 Equations of space curves
Chapter 6 The Multivariable Differential Calculus and its Applications
6.1 Limits and Continuity of Multivariable Functions
6.1.1 Primary knowledge of point sets in the space Rn
6.1.2 The concept of a multivariable function
6.1.3 Limit and continuity of multivariable functions
Exercises 6.1
6.2 Partial Derivatives and Total Differentials of Multivariable Functions
6.2.1 Partial derivatives
6.2.2 Total differentials
6.2.3 Higher-order partial derivatives
6.2.4 Directional derivatives and the gradient
Exercises 6.2
6.3 Differentiation of Multivariable Composite Functions and Implicit Functions
6.3.1 Partial derivatives and total differentials of multivariable composite functions
6.3.2 Differentiation of implicit functions defined by one equation
6.3.3 Differentiation of implicit functions determined by more than one equation
Exercises 6.3
6.4 Extreme Value Problems for Multivariable Functions
6.4.1 Unrestricted extreme values
6.4.2 Global maxima and minima
6.4.3 Extreme values with constraints; The method of Lagrange multipliers
Exercises 6.4
* 6.5 Taylors Formula for Functions of Two Variables
6.5.1 Taylors formula for functions of two variables
……
Chapter 7 The Integral Calculus of Multivariable Scalar Functions and Its Applications
Chapter 8 The Integral Calculus of Multivariable Vectorvalued Functions and its Applecations in the Theory of Fields
Chapter 9 Linear Ordinary Differential Equations
Appendix A Basic Properties of Matrices and Determinants
Appendix B Answers and Hints for Exercises 精彩书摘
5.1 Vectors and Their Linear Operations
5.1.1 The concept of vector
Some of the quantities in nature are determined completely by their magnitudes. For example, to record length, area, mass, temperature, etc., we can represent them by means of real numbers if an appropriate unit of measure is given. These quantities are called scalar quantities. But there are also some quantities in nature, such as displacement, velocity, and force, for which we need more information to describe them. To describe a displacement of a body we have to know how far it moves and in what direction. To describe the velocity of a body,we have to know where the body is headed as well as how fast it is going. To describe a force, we need to record the direction in which it acts as well as how large it is. These quantities that have both direction and magnitude, are called vectors. A vector is usually represented by a line segment with an arrow,a directed line segment. The length of the directed line segment represents the magnitude of the vector and the arrow points in the direction of the vector. The vector defined by the directed line segment from the initial point A to the terminal point B is written as AB.
图书简介:深入探索线性代数与概率统计的基石 书名: 现代应用数学导论:线性代数与概率统计基础 目标读者: 计算机科学、工程技术、经济学及其他量化分析领域的大一至大二学生,以及希望系统回顾和强化相关数学基础的自学者。 本书定位: 本书旨在提供一个坚实而全面的数学基础,重点聚焦于现代科学与工程中不可或缺的两大支柱——线性代数和概率统计。不同于传统高等数学课程对微积分的侧重,本书将这些核心工具置于应用场景的前沿,确保读者不仅理解“是什么”,更能掌握“如何用”。我们致力于搭建从抽象概念到实际问题解决的桥梁,使读者在面对复杂模型和数据分析时游刃有余。 --- 第一部分:线性代数的几何与代数统一 线性代数是理解高维空间、数据结构和算法效率的语言。本书在第一部分将严谨的理论与直观的几何理解相结合,确保读者能够从根本上掌握向量空间的操作与性质。 第一章:向量空间与基本结构 本章从最基础的向量概念入手,逐步构建起抽象的向量空间框架。我们不仅讨论$mathbb{R}^n$上的标准操作,更深入探讨抽象向量空间的定义,如函数空间、多项式空间等,拓宽读者的数学视野。关键内容包括: 线性相关性、基与维度: 严格定义并展示如何通过基的变换来简化空间描述。重点分析基的选取对计算复杂度的影响。 子空间的概念: 详细阐述四种基本子空间——列空间、零空间、行空间和左零空间——之间的深刻联系。通过对这些空间的分析,读者将领悟到矩阵运算的内在结构。 第二章:线性变换与矩阵表示 线性变换是连接不同向量空间的桥梁。本章的核心在于理解如何用矩阵来有效地表示和计算这些变换。 矩阵的本质: 将矩阵视为作用于向量的线性映射,而非仅仅是数字的集合。探讨相似变换,理解如何通过坐标系的选取(即相似矩阵)来简化线性变换的表示。 特征值与特征向量(Eigen-theory): 这是理解动态系统稳定性和数据主成分分析(PCA)的关键。我们不仅计算特征值,更侧重于解释其物理或几何意义——系统的不变方向与缩放因子。 对角化与若尔当标准型: 讨论矩阵可对角化的条件,并引入若尔当标准型来处理不可对角化的情况,为处理非线性系统的局部线性化打下基础。 第三章:正交性、分解与矩阵优化 本部分聚焦于几何直觉最强的部分——内积空间和正交性,这是优化算法和数值稳定的核心。 内积空间与Gram-Schmidt过程: 引入内积的概念,定义长度和角度。详细讲解Gram-Schmidt正交化在构建正交基中的实用性。 正交投影与最小二乘法: 这是解决超定线性方程组的唯一途径。通过几何解释,读者将理解最小二乘解即为残差向量垂直于列空间的解。 矩阵分解技术: 重点介绍QR分解(用于数值稳定性高的求解)和奇异值分解(SVD)。SVD被视为矩阵分解的“终极工具”,它揭示了矩阵固有的秩结构和最优低秩逼近,是图像压缩和推荐系统的理论基础。 --- 第二部分:概率论与数理统计:量化不确定性 在信息时代,理解和量化随机性是做出科学决策的基础。本书的第二部分从概率的基本公理出发,逐步过渡到严谨的统计推断。 第四章:概率论基础与随机变量 本章建立概率思维框架,区分离散和连续情况下的处理方式。 概率的公理化定义: 从样本空间、事件到概率测度,确保概念的严密性。 随机变量的刻画: 详细区分离散随机变量(PMF)和连续随机变量(PDF)。重点讨论期望(Expectation)和方差(Variance)的计算及其性质,特别是期望的线性性质在分析复杂度中的应用。 重要分布的精讲: 深入分析伯努利、二项、泊松、均匀、指数和正态分布。正态分布的性质及其在中心极限定理中的核心地位将被充分强调。 多维随机变量: 引入联合分布、边际分布和条件分布。协方差和相关系数的解释,帮助读者量化两个变量之间的线性关系。 第五章:极限理论与统计推断的基石 此章是连接概率模型与实际数据分析的桥梁,解释了为什么我们可以相信大样本的估计。 大数定律与中心极限定理(CLT): 这是统计学的两大支柱。详细阐述CLT如何保证在足够样本下,样本均值会趋向于正态分布,从而为构建置信区间提供理论依据。 随机变量的收敛性: 简要介绍依概率收敛和几乎必然收敛,为更高级的统计学奠定基础。 第六章:数理统计:从数据到结论 本章将理论知识应用于实际数据分析,侧重于参数估计和假设检验的构建过程。 参数估计: 点估计: 介绍估计量的优良性质(无偏性、一致性、有效性)。重点讲解矩估计法(MOM)和极大似然估计法(MLE)的计算步骤和理论优势。 区间估计: 构建置信区间,解释其含义——重复抽样中,包含真实参数的比例。重点讨论基于$t$分布、$chi^2$分布和$F$分布的区间估计。 假设检验: 系统介绍Neyman-Pearson框架。定义原假设与备择假设,理解I类错误($alpha$)和II类错误($eta$)的权衡。详细演示Z检验、t检验和方差比率检验(F检验)的完整流程。 第七章:回归分析的初步探索 本章作为统计应用的收尾,引入最基础但最重要的模型——线性回归。 简单线性回归模型: 模型的建立、最小二乘估计的推导(与线性代数中的最小二乘法呼应),以及回归系数的统计显著性检验。 模型诊断: 初步介绍残差分析的重要性,理解如何通过残差来判断模型假设是否成立。 --- 本书特色 1. 应用驱动的教学法: 每章均包含来自工程、数据科学或经济学的“应用案例聚焦”,展示如何使用SVD进行数据降维,或如何使用MLE估计金融模型参数。 2. 强调计算思维: 鼓励读者使用Python/MATLAB等工具验证理论结果,尤其是矩阵分解和蒙特卡洛模拟,将数学直觉与编程实践相结合。 3. 严谨性与可读性的平衡: 理论推导清晰、逻辑严密,同时辅以大量图示和直观解释,避免纯理论推导带来的晦涩感。 通过对线性代数和概率统计两大核心领域的深度剖析,本书为读者构建了一个强大的量化分析工具箱,为后续学习更专业的高级课程(如机器学习、优化理论、高级计量经济学)做好了充分准备。