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MATH1131 Final Review

同步 Notion 最新内容 · 重点强化第8-15题 · 全面覆盖基础知识点Synced with Notion · Enhanced Topics 8-15 · Comprehensive Coverage

📋 同步来源:Notion Final Review 更新稿 | 📅 版本:2025-01-02 | 🎯 重点:第8-15题强化更新 📋 Source: Notion Final Review | 📅 Version: 2025-01-02 | 🎯 Focus: Topics 8-15 Enhanced

术语映射表 (Terminology Mapping)

导数 → Derivative
链式法则 → Chain Rule
收敛 → Convergence
特征值 → Eigenvalue
行列式 → Determinant
矩阵乘法 → Matrix Multiplication
极坐标 → Polar Coordinates
法向量 → Normal Vector
简化行阶梯形 → RREF
微积分基本定理 → FTC
介值定理 → IVT
高斯消元 → Gaussian Elimination

1. 矩阵乘法:元素级点积计算 (Matrix Multiplication)

🎯 直观理解

矩阵乘法 $C = AB$ 的本质是将 $A$ 的每一行与 $B$ 的每一列进行点积运算。结果矩阵的每个元素 $C_{ij}$ 独立计算,仅依赖于特定的行和列。

核心公式
$$C_{ij} = \sum_{k=1}^{n} A_{ik} \cdot B_{kj} = A_{\text{第}i\text{行}} \cdot B_{\text{第}j\text{列}}$$
前提:$A$ 的列数等于 $B$ 的行数(内维匹配)
🔧 解题策略
  1. 识别目标:明确要求的元素位置 $(i,j)$
  2. 提取向量:从 $A$ 提取第 $i$ 行,从 $B$ 提取第 $j$ 列
  3. 点积计算:对应元素相乘后求和
  4. 验证检查:确认维数匹配和计算正确性
Matrix A 第1行 第4行: [7,2,3,6,6] 第n行 Matrix B 第1列 ... 第5列 [8,5,6,7,9]ᵀ Result C₄,₅ = 180 计算过程:7×8 + 2×5 + 3×6 + 6×7 + 6×9 = 56+10+18+42+54 = 180
⚠️ 常见误区
  • 维数错误:忘记检查内维匹配($A$ 的列数 = $B$ 的行数)
  • 索引混淆:将行索引和列索引颠倒
  • 计算错误:点积求和时遗漏某项或符号错误

2. 函数分析:导数、值域、反函数 (Function Analysis)

🎯 直观理解

对复合函数 $f(x) = e^{(3x+1)^2} + (3x+1)^2$ 的全面分析包括求导(链式法则)、找临界点、确定值域、求反函数存在区间以及解相关方程。

链式法则
设 $u = 3x + 1$,则:
$$f'(x) = \frac{d}{dx}[e^{u^2} + u^2] = e^{u^2} \cdot 2u \cdot 3 + 2u \cdot 3 = 6u(e^{u^2} + 1) = 6(3x+1)(e^{(3x+1)^2} + 1)$$
更新 关键分析步骤:
  1. 临界点:$f'(x) = 0 \Rightarrow 3x+1 = 0 \Rightarrow x = -\frac{1}{3}$
  2. 二阶导数测试:$x = -\frac{1}{3}$ 为极小值点
  3. 值域分析:最小值 $f(-\frac{1}{3}) = e^0 + 0 = 1$
  4. 反函数区间:在 $(-\frac{1}{3}, +\infty)$ 上严格单调递增
x f(x) 极小值 (−1/3, 1) x = −1/3 f'(x) < 0 递减 f'(x) > 0 递增 反函数存在区间
🔧 解题要点
导数计算:
  • 识别复合结构
  • 应用链式法则
  • 因式分解简化
值域确定:
  • 找所有临界点
  • 比较端点值
  • 考虑开闭性
反函数条件:
  • 确保严格单调
  • 选择最大区间
  • 包含指定点

3. 极坐标:最值与象限分析 (Polar Coordinates)

🎯 直观理解

极坐标中,点 $(r, \theta)$ 到原点的距离为 $|r|$,到坐标轴的距离通过 $x = r\cos\theta$, $y = r\sin\theta$ 计算。优化问题通常涉及在特定象限内最大化或最小化某个量。

坐标转换
$$\begin{cases} x = r\cos\theta \\ y = r\sin\theta \end{cases} \quad \begin{cases} r = \sqrt{x^2 + y^2} \\ \theta = \arctan\left(\frac{y}{x}\right) \end{cases}$$
重点 曲线 $r = \frac{10}{2+\cos\theta}$ 分析:
第二象限离 x 轴最远点:
  1. 目标:最大化 $y = r\sin\theta = \frac{10\sin\theta}{2+\cos\theta}$
  2. 约束:$\theta \in (\frac{\pi}{2}, \pi)$(第二象限)
  3. 求导:$\frac{dy}{d\theta} = \frac{10(2\cos\theta + 1)}{(2+\cos\theta)^2} = 0$
  4. 解得:$\cos\theta = -\frac{1}{2} \Rightarrow \theta = \frac{2\pi}{3}$
  5. 最大值:$y_{\max} = \frac{10\sqrt{3}}{3}$
x y O I II III IV 最远点 y = 10√3/3 θ = 2π/3 到x轴距离
🔧 解题策略
最值问题:
  1. 明确目标函数($r$, $x$, $y$)
  2. 确定约束条件(象限、区间)
  3. 求导并找临界点
  4. 比较端点和临界点
常见目标:
  • 离原点最远/近:优化 $r$
  • 离x轴最远:优化 $|y|$
  • 离y轴最远:优化 $|x|$
  • 特定象限:添加角度约束

第四题:平面与向量分析 (Plane and Vector Analysis)

向量叉积、点积、点到平面距离、中间值定理

涉及概念 (Concepts Covered)

向量运算

  • 向量叉积 (Vector Cross Product)
  • 向量点积 (Vector Dot Product)
  • 点到平面的距离 (Distance from Point to Plane)

定理应用

  • 中间值定理 (Intermediate Value Theorem)
  • 法向量计算
  • 侧别判断

解题步骤 (Steps)

(a) 判断点是否在平面上

核心理念 (Core Idea)

如果一个点 $P_i$ 在平面 $\Pi$ 上,那么从平面上任一点 $A$ 到 $P_i$ 的向量 $\overrightarrow{AP_i}$ 应该与平面的法向量 $\boldsymbol{n}$ 垂直。

判断条件

两个向量垂直,其点积为 0:

$$\overrightarrow{AP_i} \cdot \boldsymbol{n} = 0$$

平面的法向量 $\boldsymbol{n}$ 可以通过平面上的两个向量的叉积得到:

$$\boldsymbol{n} = \overrightarrow{AB} \times \overrightarrow{AC}$$

解题 (Solution)

题目提供了所有点的点积值。我们只需要找出点积结果为 0 的点。

通过观察题目数据:

  • $\overrightarrow{AP_2} \cdot (\overrightarrow{AB} \times \overrightarrow{AC}) = 0$
  • $\overrightarrow{AP_6} \cdot (\overrightarrow{AB} \times \overrightarrow{AC}) = 0$

结论:点 P₂ 和 P₆ 在平面 Π 上

(b) 计算最短距离

核心理念 (Core Idea)

一个点 $P_i$ 到平面 $\Pi$ 的最短距离,等于向量 $\overrightarrow{AP_i}$ 在平面法向量 $\boldsymbol{n}$ 方向上的投影长度。

距离公式

$$d = \frac{|\overrightarrow{AP_i} \cdot \boldsymbol{n}|}{|\boldsymbol{n}|} = \frac{|\overrightarrow{AP_i} \cdot (\overrightarrow{AB} \times \overrightarrow{AC})|}{|\overrightarrow{AB} \times \overrightarrow{AC}|}$$

分母计算

三角形 ABC 的面积是平行四边形面积的一半,而平行四边形面积等于叉积的模。

$$\text{Area}_{ABC} = \frac{1}{2}|\overrightarrow{AB} \times \overrightarrow{AC}|$$

题目给出 $\text{Area}_{ABC} = \frac{\sqrt{29}}{2}$,所以 $|\overrightarrow{AB} \times \overrightarrow{AC}| = \sqrt{29}$

计算各点距离
  • $d_1 = \frac{21}{\sqrt{29}} = \frac{21\sqrt{29}}{29}$
  • $d_3 = \frac{51}{\sqrt{29}} = \frac{51\sqrt{29}}{29}$
  • $d_4 = \frac{12}{\sqrt{29}} = \frac{12\sqrt{29}}{29}$
  • $d_5 = \frac{57}{\sqrt{29}} = \frac{57\sqrt{29}}{29}$

结论:最短距离是 $\frac{12}{\sqrt{29}}$,Maple 语法为 4/sqrt(29)

(c) 判断点在平面的哪一侧

核心理念 (Core Idea)

向量 $\overrightarrow{AP_i}$ 与平面法向量 $\boldsymbol{n}$ 的点积符号,决定了点 $P_i$ 位于平面的哪一侧。

  • 点积为,表示在平面的某一侧
  • 点积为,表示在平面的另一侧
解题 (Solution)

我们已知 P₁ 的点积为 $\overrightarrow{AP_1} \cdot \boldsymbol{n} = -21$,符号为负。

我们需要找出点积结果为正数的点(符号与 P₁ 相反):

  • $\overrightarrow{AP_3} \cdot \boldsymbol{n} = 51$(正数)
  • $\overrightarrow{AP_5} \cdot \boldsymbol{n} = 57$(正数)

结论:点 P₃ 和 P₅ 与 P₁ 位于平面的相反一侧

(d) 中间值定理证明

核心理念 (Core Idea)

如果一条连续的曲线的两个端点位于平面的相反两侧,那么这条曲线必然会穿过这个平面。

证明步骤
  1. 定义函数:设函数 $f(t) = \overrightarrow{AR(t)} \cdot (\overrightarrow{AB} \times \overrightarrow{AC})$
  2. 满足定理假设:
    • 连续性:$R(t)$ 是连续函数,所以 $f(t)$ 也是连续
    • 端点值:$P = R(0)$ 和 $Q = R(1)$ 位于平面的相反两侧,所以 $f(0)$ 和 $f(1)$ 的符号相反
  3. 应用定理:根据中间值定理,在 $[0,1]$ 区间内必然存在一个 $c$,使得 $f(c) = 0$
  4. 结论:$f(c) = \overrightarrow{AR(c)} \cdot (\overrightarrow{AB} \times \overrightarrow{AC}) = 0$ 意味着点 $R(c)$ 位于平面上

5. 矩阵与线性方程组 5. Matrices and Linear Systems

核心理念 Core Concept

矩阵的简化行阶梯形(RREF)是矩阵的标准形式,通过Gauss-Jordan消元法获得。可逆矩阵与行列式非零等价。

Reduced Row Echelon Form (RREF) is the standard form of a matrix, obtained through Gauss-Jordan elimination. Matrix invertibility is equivalent to non-zero determinant.

关键公式 (Key Formulas)
  • 逆的转置: $(A^T)^{-1} = (A^{-1})^T$
  • 行列式性质: $\det(AB) = \det(A)\det(B)$
  • 可逆条件: $A$ 可逆 $\Leftrightarrow$ $\det(A) \neq 0$
  • RREF目标:主元为1,主元列其他元素为0

(a)(i) 简化行阶梯形 (RREF)

题目 (Problem)

求矩阵 $A$ 的简化行阶梯形(RREF)。

解题步骤 (Solution Steps)
1

RREF构造步骤:

  • 行消元:将第一列第一个非零元素变为1(主元)
  • 列清零:将主元列的其他元素变为0
  • 移至下行:对剩余子矩阵重复上述过程
  • 最终检查:确保每个主元为1,且主元列其他元素为0

(a)(ii) 求转置的逆矩阵 (Inverse of Transpose)

题目 (Problem)

已知 $A^{-1}$,求 $(A^T)^{-1}$。

核心公式 (Key Formula)

$(A^T)^{-1} = (A^{-1})^T$

解题方法 (Solution Method)
  1. 已知 $A^{-1}$,直接对其取转置
  2. 结果即为 $(A^T)^{-1}$

(b)(i) 找出不一致的 m 值 (Find Inconsistent m)

题目 (Problem)

线性系统 $Ax = e_3$ 中,矩阵含参数 $m$,找出使系统不一致(无解)的 $m$ 值。

判断标准 (Criteria)

无解条件:存在行 $[0 \cdots 0 \mid c \neq 0]$(矛盾行)

解题步骤 (Solution Steps)
  1. 构造增广矩阵 $[A|e_3]$
  2. 执行行操作至REF
  3. 找到临界行:$[0 \quad 0 \quad m(m+3) \mid m+3]$
  4. 分析:当左边系数为0但右边非0时,系统不一致
分析结果 (Analysis Result)
  • m = 0:$[0 \quad 0 \quad 0 \mid 3]$ → 矛盾!无解

结论:m = 0 时系统不一致

(b)(ii) 找出一致的 m 值 (Find Consistent m)

题目 (Problem)

找出使系统一致(有解)的 $m$ 值。

判断标准 (Criteria)

一致条件:不存在矛盾行(即 $[0 \cdots 0 \mid c \neq 0]$ 不出现)

解题步骤 (Solution Steps)

基于临界行 $[0 \quad 0 \quad m(m+3) \mid m+3]$ 的分析:

  • m = 0:$[0 \quad 0 \quad 0 \mid 3]$ → 矛盾,不一致
  • m = -3:$[0 \quad 0 \quad 0 \mid 0]$ → 一致(无穷解)
  • m ≠ 0, -3:主元非零 → 一致(唯一解)
结论 (Conclusion)

所有 m ≠ 0 的值都使系统一致

(c) 解集不同的 m 值 (Different Solution Sets)

题目 (Problem)

找出使解集具有不同性质的 $m$ 值,并描述解的情况。

解的分类 (Solution Classification)
  • 无解:存在矛盾行 $[0 \cdots 0 \mid c \neq 0]$
  • 唯一解:主元数 = 变量数,且系统一致
  • 无穷解:主元数 < 变量数,且系统一致
完整分析 (Complete Analysis)

m = 0:

无解(不一致)

m = -3:

无穷多解(一致,有自由变量)

m ≠ 0, -3:

唯一解(一致,主元满秩)

6. 高斯消元法与线性方程组 6. Gaussian Elimination and Linear Systems

核心理念 Core Concept

线性方程组 $Ax = b$ 的解的情况完全由其增广矩阵的行阶梯形决定。关键是识别矛盾行和自由变量的存在。

The solutions of linear system $Ax = b$ are determined by the row echelon form of its augmented matrix. Key is identifying contradictory rows and free variables.

解的分类 (Solution Classification)
  • 无解:存在行 $[0 \cdots 0 \mid c \neq 0]$
  • 唯一解:主元数 = 变量数,且系统一致
  • 无穷解:主元数 < 变量数,且系统一致
  • 几何解释:3D中,一个线性方程表示一个平面

(a) 解线性方程组 (Solve Linear System)

题目 (Problem)

使用高斯消元法解线性方程组 $Ax = b$。

解题步骤 (Solution Steps)
1

构造增广矩阵

$[A|b]$

2

执行行操作至REF/RREF

  • 将第一列第一个非零元素变为1(主元)
  • 将主元列的其他元素变为0
  • 对剩余子矩阵重复上述过程
3

识别主元位置并检查矛盾行

4

根据RREF形式判断解的情况

(b) 参数方程组分析 (Parametric System Analysis)

题目 (Problem)

系统 $Ax = e_3$,其中矩阵含参数 $m$,分析不同 $m$ 值对应的解的情况。

分析关键 (Analysis Key)

临界行:$[0 \quad 0 \quad m(m+3) \mid m+3]$

该行的左边系数和右边常数决定了系统的性质:

分情况讨论 (Case Analysis)

$m = 0$:

$[0 \quad 0 \quad 0 \mid 3]$ → 矛盾行 → 无解

$m = -3$:

$[0 \quad 0 \quad 0 \mid 0]$ → 自由变量 → 无穷解

$m \neq 0, -3$:

主元非零 → 唯一解

(c) 几何解释 (Geometric Interpretation)

3D空间中的线性方程
  • 每个线性方程表示一个平面
  • 解 = 平面的交集
  • 唯一解:三个平面交于一点
  • 无穷解:三个平面交于一条直线或整个平面
  • 无解:三个平面没有公共交点

7. 微积分基本定理与积分函数 7. Fundamental Theorem of Calculus and Integral Functions

核心理念 Core Concept

微积分基本定理建立了导数与积分的联系。对于积分函数 $G(x) = \int_a^x g(t)dt$,有 $G'(x) = g(x)$。面积解释:$G(x)$ 表示曲线下的代数面积。

The Fundamental Theorem of Calculus establishes the connection between derivatives and integrals. For integral function $G(x) = \int_a^x g(t)dt$, we have $G'(x) = g(x)$. Area interpretation: $G(x)$ represents the algebraic area under the curve.

基本定理 (Fundamental Theorems)
  • FTC I:若 $G(x) = \int_a^x g(t)dt$,则 $G'(x) = g(x)$
  • 面积计算:$\int_a^b g(t)dt = $ 上方面积 $-$ 下方面积
  • 换限规则:$\int_a^b f(t)dt = -\int_b^a f(t)dt$

(a) 积分函数的导数 (Derivative of Integral Function)

题目 (Problem)

给定 $G(x) = \int_1^x g(t)dt$,求 $G'(x)$。

直接应用 FTC I

$G'(x) = g(x)$

(b) 计算特定点的值 (Calculate Values at Specific Points)

题目 (Problem)

计算 $G(-1)$、$G(6)$ 等特定点的值。

面积计算策略 (Area Calculation Strategy)
  • 三角形:$\frac{1}{2} \times \text{底} \times \text{高}$
  • 矩形:$\text{长} \times \text{宽}$
  • 梯形:$\frac{1}{2}(b_1 + b_2) \times h$
  • 符号规则:上方为正,下方为负
关键计算 (Key Calculations)
  • $G(-1) = -6$(三角形面积,换限变号)
  • $G(6) = 1.5$(分段计算几何面积)

(c) 积分函数的极值 (Extrema of Integral Function)

题目 (Problem)

给定 $G(x) = \int_1^x g(t)dt$,$g(x)$ 在 $[-1,6]$ 连续,求 $G(x)$ 的全局最大值。

极值寻找步骤 (Extrema Finding Steps)
  1. 找临界点:$G'(x) = g(x) = 0$
  2. 计算临界点值:$G(\frac{10}{3}) = \frac{25}{6}$
  3. 比较端点值:$G(-1) = -6$,$G(6) = 1.5$
  4. 确定全局最值:比较所有候选值
结论 (Conclusion)

全局最大值在 $x = \frac{10}{3}$ 处取得,值为 $\frac{25}{6}$

8. 矩阵的对称性 8. Matrix Symmetry

核心理念 Core Concept

对称矩阵满足 $A^T = A$。两个矩阵的乘积 $AB$ 的对称性取决于 $A$ 和 $B$ 的关系。当 $A = B$ 时,$A^2$ 总是对称的。

A symmetric matrix satisfies $A^T = A$. The symmetry of product $AB$ depends on the relationship between $A$ and $B$. When $A = B$, $A^2$ is always symmetric.

关键公式 (Key Formulas)
  • 转置性质:$(AB)^T = B^T A^T$
  • 对称条件:$A$ 对称 $\Leftrightarrow$ $A^T = A$
  • 乘积对称:$(A^2)^T = (A \cdot A)^T = A^T \cdot A^T = A \cdot A = A^2$

(a) 矩阵乘积的对称性 (Symmetry of Matrix Product)

题目 (Problem)

给定对称矩阵 $A$,判断 $A^2$ 是否对称。

证明 (Proof)
  1. 已知 $A^T = A$($A$ 是对称矩阵)
  2. $(A^2)^T = (A \cdot A)^T = A^T \cdot A^T$(转置的乘积性质)
  3. $= A \cdot A = A^2$(因为 $A^T = A$)
  4. 因此 $(A^2)^T = A^2$,即 $A^2$ 是对称矩阵
结论 (Conclusion)

$A^2$ 是对称矩阵

9. 向量运算的定义 9. Vector Operation Definitions

核心理念 Core Concept

向量的点积和叉积是两种基本的向量运算,它们在几何和物理中有广泛应用。点积结果是标量,叉积结果是向量。

Dot product and cross product are two fundamental vector operations with wide applications in geometry and physics. Dot product yields a scalar, cross product yields a vector.

关键公式 (Key Formulas)
  • 点积(内积):$\boldsymbol{a} \cdot \boldsymbol{b} = |\boldsymbol{a}||\boldsymbol{b}|\cos\theta = a_1b_1 + a_2b_2 + a_3b_3$
  • 叉积(外积):$\boldsymbol{a} \times \boldsymbol{b}$ 垂直于 $\boldsymbol{a}$ 和 $\boldsymbol{b}$,大小为 $|\boldsymbol{a}||\boldsymbol{b}|\sin\theta$
  • 叉积公式:$\boldsymbol{a} \times \boldsymbol{b} = \begin{vmatrix} \boldsymbol{i} & \boldsymbol{j} & \boldsymbol{k} \\ a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \end{vmatrix}$

(a) 点积的几何意义 (Geometric Meaning of Dot Product)

题目 (Problem)

解释点积 $\boldsymbol{a} \cdot \boldsymbol{b}$ 的几何意义。

几何解释 (Geometric Interpretation)
  • 投影解释:$\boldsymbol{a} \cdot \boldsymbol{b} = |\boldsymbol{a}| \times$ ($\boldsymbol{b}$ 在 $\boldsymbol{a}$ 上的投影)
  • 角度关系:$\cos\theta = \frac{\boldsymbol{a} \cdot \boldsymbol{b}}{|\boldsymbol{a}||\boldsymbol{b}|}$
  • 垂直条件:$\boldsymbol{a} \perp \boldsymbol{b} \Leftrightarrow \boldsymbol{a} \cdot \boldsymbol{b} = 0$

(b) 叉积的性质 (Properties of Cross Product)

题目 (Problem)

列出叉积的主要性质。

关键性质 (Key Properties)
  • 反交换律:$\boldsymbol{a} \times \boldsymbol{b} = -(\boldsymbol{b} \times \boldsymbol{a})$
  • 分配律:$\boldsymbol{a} \times (\boldsymbol{b} + \boldsymbol{c}) = \boldsymbol{a} \times \boldsymbol{b} + \boldsymbol{a} \times \boldsymbol{c}$
  • 自叉积为零:$\boldsymbol{a} \times \boldsymbol{a} = \boldsymbol{0}$
  • 平行条件:$\boldsymbol{a} \parallel \boldsymbol{b} \Leftrightarrow \boldsymbol{a} \times \boldsymbol{b} = \boldsymbol{0}$

10. 函数的反函数与微积分 10. Inverse Functions and Calculus

核心理念 Core Concept

反函数的导数与原函数的导数有倒数关系。若 $f$ 可逆且 $f'(a) \neq 0$,则 $(f^{-1})'(f(a)) = \frac{1}{f'(a)}$。

The derivative of inverse function has a reciprocal relationship with the original function's derivative. If $f$ is invertible and $f'(a) \neq 0$, then $(f^{-1})'(f(a)) = \frac{1}{f'(a)}$.

关键公式 (Key Formulas)
  • 反函数导数:$(f^{-1})'(f(a)) = \frac{1}{f'(a)}$
  • 链式法则:$\frac{d}{dx}f^{-1}(f(x)) = (f^{-1})'(f(x)) \cdot f'(x) = 1$

(a) 反函数存在性 (Existence of Inverse Function)

题目 (Problem)

判断函数 $f$ 是否有反函数。

判断标准 (Criteria)
  • 一一对应:函数必须是双射(单射+满射)
  • 单调性:严格单调函数必有反函数
  • 水平线测试:任意水平线最多与图像交于一点

(b) 反函数的导数 (Derivative of Inverse Function)

题目 (Problem)

求 $f^{-1}$ 在某点的导数。

解题步骤 (Solution Steps)
  1. 确定 $y = f(x)$,求 $f'(x)$
  2. 应用公式:$(f^{-1})'(y) = \frac{1}{f'(x)}$
  3. 代入具体数值计算
注意 (Note)

反函数导数的求值点是 $f(a)$,不是 $a$。

11. 下黎曼和 11. Lower Riemann Sum

核心理念 Core Concept

下黎曼和使用每个子区间上的最小值作为矩形高度,是对定积分的下界估计。随着分割加密,下黎曼和趋近于真实积分值。

Lower Riemann sum uses the minimum value on each subinterval as rectangle height, providing a lower bound estimate of the definite integral. As partition refines, the sum approaches the true integral value.

定义公式 (Definition Formula)

将区间 $[a,b]$ 分成 $n$ 个子区间 $[x_{i-1}, x_i]$,下黎曼和为:

$$L_n = \sum_{i=1}^{n} f(m_i) \cdot \Delta x_i$$

其中 $m_i$ 是 $f$ 在 $[x_{i-1}, x_i]$ 上的最小值点。

(a) 等分区间 (Equal Partitions)

题目 (Problem)

将区间 $[a,b]$ 等分成 $n$ 份,计算下黎曼和。

解题步骤 (Solution Steps)
  1. 计算步长:$\Delta x = \frac{b-a}{n}$
  2. 确定分点:$x_i = a + i \cdot \Delta x$,$i = 0, 1, \ldots, n$
  3. 找每个子区间的最小值:分析 $f$ 的单调性
  4. 求和:$L_n = \sum_{i=1}^{n} f(m_i) \cdot \Delta x$

(b) 收敛性 (Convergence)

题目 (Problem)

证明当 $n \to \infty$ 时,$L_n \to \int_a^b f(x)dx$。

关键定理 (Key Theorem)

若 $f$ 在 $[a,b]$ 上连续,则:

$$\lim_{n \to \infty} L_n = \int_a^b f(x)dx$$

12. 函数极限与反常积分 12. Function Limits and Improper Integrals

核心理念 Core Concept

反常积分处理无穷区间或无界函数的积分。收敛性取决于被积函数在无穷远处或奇点附近的"衰减速度"。

Improper integrals handle integrals over infinite intervals or with unbounded functions. Convergence depends on the "decay rate" of the integrand at infinity or near singularities.

反常积分类型 (Types of Improper Integrals)
  • 无穷区间:$\int_a^{\infty} f(x)dx = \lim_{b \to \infty} \int_a^b f(x)dx$
  • 无界函数:$\int_a^b f(x)dx = \lim_{\epsilon \to 0^+} \int_{a+\epsilon}^b f(x)dx$
  • p-积分:$\int_1^{\infty} \frac{1}{x^p}dx$ 当 $p > 1$ 时收敛

(a) 极限计算 (Limit Calculation)

题目 (Problem)

计算 $\lim_{x \to a} f(x)$。

常用方法 (Common Methods)
  • 直接代入:若 $f$ 在 $a$ 连续
  • 洛必达法则:$\frac{0}{0}$ 或 $\frac{\infty}{\infty}$ 型
  • 等价无穷小:当 $x \to 0$ 时,$\sin x \sim x$,$\ln(1+x) \sim x$

(b) 反常积分收敛性 (Convergence of Improper Integral)

题目 (Problem)

判断 $\int_a^{\infty} f(x)dx$ 是否收敛。

判别方法 (Convergence Tests)
  • 比较判别法:若 $0 \leq f(x) \leq g(x)$ 且 $\int g$ 收敛,则 $\int f$ 收敛
  • 极限比较法:若 $\lim_{x \to \infty} \frac{f(x)}{g(x)} = L \in (0, \infty)$,则两者同敛散
  • p-判别法:$\int_1^{\infty} \frac{1}{x^p}dx$ 当 $p > 1$ 收敛,$p \leq 1$ 发散

第13题:矩阵逆的转置

Notion题13
题面

矩阵 $(A^T)^{-1}$ 等于?

重要性质证明

结论:$(A^T)^{-1} = (A^{-1})^T$

证明思路:
  1. 由 $AA^{-1} = I$ 开始
  2. 两边同时取转置:$(AA^{-1})^T = I^T$
  3. 利用转置性质:$(A^{-1})^T (A^T) = I$
  4. 由逆的唯一性:$(A^T)^{-1} = (A^{-1})^T$
答案:$(A^{-1})^T$
记忆技巧

"逆的转置 = 转置的逆",操作顺序可以交换。

第14题:系统一致性

Notion题14
题面

线性方程组 $Ax = b$ 无解的充要条件是在其增广矩阵的行阶梯形中出现?

无解特征

关键行模式:$[0 \quad 0 \quad \cdots \quad 0 \mid c \neq 0]$

解释:
  • 左侧全零表示没有变量
  • 右侧非零表示方程为 $0 = c$ (矛盾)
  • 这样的方程显然无解
答案:形如 $[0 \cdots 0 \mid c \neq 0]$ 的行
增广矩阵行阶梯形 [ 1 2 3 | 5 ] [ 0 1 2 | -1 ] [ 0 0 0 | 7 ] ← 矛盾行 矛盾:0 = 7 系统无解

第15题:几何解释

Notion题15
题面

三维空间中,方程 $x + 2y + 4z = 45$ 的几何解释是?

几何意义

答案:一个平面

一般性理解:
  • 形如 $ax + by + cz = d$ 的方程表示3D空间中的平面
  • 系数 $(a,b,c)$ 构成平面的法向量
  • 常数 $d$ 决定平面与原点的距离
本题分析:
  • 法向量:$\boldsymbol{n} = (1, 2, 4)$
  • 该平面不过原点($d = 45 \neq 0$)
x y z x+2y+4z=45 n=(1,2,4) 每个线性方程在3D空间中表示一个平面

📝 更新日志

2025年8月13日 同步更新
  • 完成第8-15题同步 严格按照 Notion 最新版本更新题面、解法、易错点。
  • 增强可视化 新增 SVG 图示,涵盖矩阵乘法、函数分析、极坐标、向量平面等。
🎯 覆盖知识点清单
✅ 矩阵乘法
✅ 复合函数求导
✅ 极坐标系统
✅ 向量与平面
✅ 行列式性质
✅ 线性系统解法
✅ RREF构造
✅ 可逆性判定
✅ 微积分基本定理
✅ 几何解释
✅ 介值定理应用
✅ 参数方程讨论
📚 学习建议
  1. 结合练习:本笔记与配套的50题测验相互对照学习
  2. 概念图示:重点关注SVG图示,建立几何直观
  3. 易错提醒:每个模块的"易错点"部分需特别注意
  4. 公式记忆:利用术语映射表强化中英文对照记忆

Terminology Mapping

Derivative → 导数
Chain Rule → 链式法则
Convergence → 收敛
Eigenvalue → 特征值
Determinant → 行列式
Matrix Multiplication → 矩阵乘法
Polar Coordinates → 极坐标
Normal Vector → 法向量
RREF → 简化行阶梯形
FTC → 微积分基本定理
IVT → 介值定理
Gaussian Elimination → 高斯消元

📑 Table of Contents

1. Matrix Multiplication: Element-wise Dot Product

🎯 Intuitive Understanding

Matrix multiplication $C = AB$ essentially computes the dot product of each row of $A$ with each column of $B$. Each element $C_{ij}$ of the result matrix is calculated independently, depending only on specific rows and columns.

Core Formula
$$C_{ij} = \sum_{k=1}^{n} A_{ik} \cdot B_{kj} = A_{\text{row }i} \cdot B_{\text{column }j}$$
Prerequisite: Number of columns in $A$ equals number of rows in $B$ (inner dimension matching)
🔧 Problem-Solving Strategy
  1. Identify Target: Clarify the element position $(i,j)$ to find
  2. Extract Vectors: Get row $i$ from $A$, column $j$ from $B$
  3. Dot Product: Multiply corresponding elements and sum
  4. Verification: Confirm dimension matching and calculation correctness
Matrix A Row 1 Row 4: [7,2,3,6,6] Row n Matrix B Col 1 ... Col 5 [8,5,6,7,9]ᵀ Result C₄,₅ = 180 Calculation: 7×8 + 2×5 + 3×6 + 6×7 + 6×9 = 56+10+18+42+54 = 180
⚠️ Common Mistakes
  • Dimension Errors: Forgetting to check inner dimension matching ($A$'s columns = $B$'s rows)
  • Index Confusion: Reversing row and column indices
  • Calculation Errors: Missing terms or sign errors during dot product summation

2. Function Analysis: Derivatives, Range, Inverse Functions

🎯 Intuitive Understanding

Comprehensive analysis of composite function $f(x) = e^{(3x+1)^2} + (3x+1)^2$ includes differentiation (chain rule), finding critical points, determining range, finding inverse function intervals, and solving related equations.

Chain Rule
Let $u = 3x + 1$, then:
$$f'(x) = \frac{d}{dx}[e^{u^2} + u^2] = e^{u^2} \cdot 2u \cdot 3 + 2u \cdot 3 = 6u(e^{u^2} + 1) = 6(3x+1)(e^{(3x+1)^2} + 1)$$
Key Analysis Steps:
  1. Critical Point: $f'(x) = 0 \Rightarrow 3x+1 = 0 \Rightarrow x = -\frac{1}{3}$
  2. Second Derivative Test: $x = -\frac{1}{3}$ is a minimum point
  3. Range Analysis: Minimum value $f(-\frac{1}{3}) = e^0 + 0 = 1$
  4. Inverse Function Interval: Strictly increasing on $(-\frac{1}{3}, +\infty)$
🔧 Key Points
Derivative Calculation:
  • Identify composite structure
  • Apply chain rule
  • Factor and simplify
Range Determination:
  • Find all critical points
  • Compare endpoint values
  • Consider open/closed nature
Inverse Conditions:
  • Ensure strict monotonicity
  • Choose maximum interval
  • Include specified point

3. Polar Coordinates: Max/Min & Quadrant Analysis

🎯 Intuitive Understanding

In polar coordinates, point $(r, \theta)$ has distance $|r|$ from origin. Distances to axes are calculated via $x = r\cos\theta$, $y = r\sin\theta$. Optimization problems typically involve maximizing or minimizing some quantity within specific quadrants.

Coordinate Conversion
$$\begin{cases} x = r\cos\theta \\ y = r\sin\theta \end{cases} \quad \begin{cases} r = \sqrt{x^2 + y^2} \\ \theta = \arctan\left(\frac{y}{x}\right) \end{cases}$$
Key Curve $r = \frac{10}{2+\cos\theta}$ Analysis:
Furthest point from x-axis in Quadrant II:
  1. Goal: Maximize $y = r\sin\theta = \frac{10\sin\theta}{2+\cos\theta}$
  2. Constraint: $\theta \in (\frac{\pi}{2}, \pi)$ (Quadrant II)
  3. Derivative: $\frac{dy}{d\theta} = \frac{10(2\cos\theta + 1)}{(2+\cos\theta)^2} = 0$
  4. Solve: $\cos\theta = -\frac{1}{2} \Rightarrow \theta = \frac{2\pi}{3}$
  5. Maximum: $y_{\max} = \frac{10\sqrt{3}}{3}$
🔧 Problem-Solving Strategy
Optimization Problems:
  1. Define objective function ($r$, $x$, $y$)
  2. Determine constraints (quadrant, interval)
  3. Find derivative and critical points
  4. Compare endpoints and critical points
Common Objectives:
  • Furthest/nearest from origin: optimize $r$
  • Furthest from x-axis: optimize $|y|$
  • Furthest from y-axis: optimize $|x|$
  • Specific quadrant: add angle constraints

4. Vectors & Planes: Normal Vectors & Distance

🎯 Intuitive Understanding

A plane can be determined by three non-collinear points, with its normal vector calculated via cross product. Point-to-plane distance uses vector projection principles, and side determination uses dot product sign.

Core Formulas
Normal Vector: $\boldsymbol{n} = \overrightarrow{AB} \times \overrightarrow{AC}$
Point on Plane: $\overrightarrow{AP} \cdot \boldsymbol{n} = 0$
Point-Plane Distance: $d = \frac{|\overrightarrow{AP} \cdot \boldsymbol{n}|}{|\boldsymbol{n}|}$
Area Relation: $\text{Area}_{ABC} = \frac{1}{2}|\boldsymbol{n}|$
Key Finding Distance from Known Area:

If $\text{Area}_{ABC} = \frac{\sqrt{29}}{2}$, then $|\boldsymbol{n}| = 2 \times \frac{\sqrt{29}}{2} = \sqrt{29}$

For a point with dot product value $-12$: $d = \frac{|-12|}{\sqrt{29}} = \frac{12}{\sqrt{29}}$

🔧 Solution Process
Calculate Normal Vector:
  1. Select three points A, B, C on plane
  2. Calculate $\overrightarrow{AB}$ and $\overrightarrow{AC}$
  3. Find cross product for normal vector
  4. Verify: any point on plane gives dot product 0
Distance & Side:
  1. Calculate $\overrightarrow{AP} \cdot \boldsymbol{n}$
  2. Distance: absolute value divided by $|\boldsymbol{n}|$
  3. Side: compare dot product signs
  4. Special: known area can reverse-calculate $|\boldsymbol{n}|$
🧮 Intermediate Value Theorem Application

Continuous curve intersecting plane: If curve $R(t)$ is continuous, and endpoints are on opposite sides of the plane, then there exists $t = c$ such that $R(c)$ is on the plane.

Proof idea: Define $f(t) = \overrightarrow{AR(t)} \cdot \boldsymbol{n}$, by continuity and IVT, there exists $c$ such that $f(c) = 0$.

5. Matrix Algebra: RREF & Invertibility

🎯 Intuitive Understanding

Reduced Row Echelon Form (RREF) is the canonical form of a matrix, obtained through Gauss-Jordan elimination. Matrix invertibility is equivalent to non-zero determinant, with important algebraic properties.

Key Properties
Inverse Transpose: $(A^T)^{-1} = (A^{-1})^T$
Determinant Property: $\det(AB) = \det(A)\det(B)$
Invertibility Condition: $A$ invertible $\Leftrightarrow$ $\det(A) \neq 0$
RREF Goal: Pivots are 1, other elements in pivot columns are 0
Application Composite Matrix Invertibility:

Question: If $\det(C) \neq 0$, what's the necessary and sufficient condition for $AC$ to be non-invertible?

Analysis: $\det(AC) = \det(A)\det(C) = 0 \Rightarrow \det(A) = 0$ (since $\det(C) \neq 0$)

Conclusion: $AC$ is non-invertible if and only if $\det(A) = 0$

🔧 RREF Construction Steps
  1. Row Operation: Make first non-zero element in first column equal to 1 (pivot)
  2. Column Clear: Make other elements in pivot column equal to 0
  3. Move to Next Row: Repeat for remaining submatrix
  4. Final Check: Ensure each pivot is 1, and other elements in pivot columns are 0

6. Linear Systems: Solution Existence

🎯 Intuitive Understanding

The solution status of linear system $Ax = b$ is completely determined by its augmented matrix's row echelon form. Key is identifying contradictory rows and presence of free variables.

Solution Classification
No Solution: Row exists of form $[0 \cdots 0 \mid c \neq 0]$
Unique Solution: Number of pivots = number of variables, system consistent
Infinite Solutions: Number of pivots < number of variables, system consistent
Geometric Interpretation: In 3D, one linear equation represents a plane
Analysis Parametric System:

System: $Ax = e_3$, where matrix contains parameter $m$

Critical Row: $[0 \quad 0 \quad m(m+3) \mid m+3]$

$m = 0$: $[0 \quad 0 \quad 0 \mid 3] \Rightarrow$ No solution
$m = -3$: $[0 \quad 0 \quad 0 \mid 0] \Rightarrow$ Infinite solutions
$m \neq 0, -3$: Pivot non-zero $\Rightarrow$ Unique solution
🔧 System Analysis Process
Matrix Operations:
  1. Construct augmented matrix $[A|b]$
  2. Perform row operations to REF/RREF
  3. Identify pivot positions
  4. Check for contradictory rows
Solution Determination:
  1. Contradictory row? → No solution
  2. Pivots = variables? → Unique solution
  3. Pivots < variables? → Infinite solutions
  4. Parameter discussion: case-by-case analysis

7. Fundamental Theorem of Calculus: Integral Functions

🎯 Intuitive Understanding

The Fundamental Theorem of Calculus establishes the connection between derivatives and integrals. For integral function $G(x) = \int_a^x g(t)dt$, we have $G'(x) = g(x)$. Area interpretation: $G(x)$ represents the signed area under the curve.

Fundamental Theorem
FTC I: If $G(x) = \int_a^x g(t)dt$, then $G'(x) = g(x)$
Area Calculation: $\int_a^b g(t)dt = $ Upper area $-$ Lower area
Limit Swap: $\int_a^b f(t)dt = -\int_b^a f(t)dt$
Key Extrema of Integral Function:

Given: $G(x) = \int_1^x g(t)dt$, $g(x)$ continuous on $[-1,6]$

Key Values:
  • $G(-1) = -6$ (triangle area, sign change from limit swap)
  • $G(6) = 1.5$ (segmented geometric area calculation)
  • $G(\frac{10}{3}) = \frac{25}{6}$ (value at critical point)
Global Maximum: Achieved at $x = \frac{10}{3}$
🔧 Area Calculation Strategy
Geometric Area:
  • Triangle: $\frac{1}{2} \times \text{base} \times \text{height}$
  • Rectangle: $\text{length} \times \text{width}$
  • Trapezoid: $\frac{1}{2}(b_1 + b_2) \times h$
  • Above axis is positive, below is negative
Finding Extrema:
  • Find critical points: $G'(x) = g(x) = 0$
  • Compare endpoint values
  • Determine global extrema
  • Note open/closed interval boundaries

8. Problem 8: Determinant & Invertibility

🎯 Intuitive Understanding

The invertibility of a matrix product is determined by the determinants of individual factors. When one factor is known to be invertible, the non-invertibility condition of the whole is completely determined by other factors.

Key Formula
$$\det(AC) = \det(A) \cdot \det(C)$$
The determinant of a matrix product equals the product of determinants
🔧 Problem-Solving Strategy
  1. Identify Given Conditions: $\det(C) \neq 0$ means $C$ is invertible
  2. Apply Determinant Property: $\det(AC) = \det(A) \cdot \det(C)$
  3. Analyze Non-invertibility: $AC$ non-invertible $\Leftrightarrow$ $\det(AC) = 0$
  4. Logical Deduction: Since $\det(C) \neq 0$, must have $\det(A) = 0$
⚠️ Common Mistakes
  • Ignoring Given Conditions: Forgetting key info $\det(C) \neq 0$
  • Condition Direction Confusion: Misunderstanding necessary and sufficient conditions
  • Determinant Calculation Errors: Mistakenly thinking need to calculate specific determinant values

9. Problem 9: Composite Function Equations

🎯 Intuitive Understanding

Through clever variable substitution, transform complex composite function equations into simple monotonic function equations, using monotonicity to ensure solution uniqueness.

Substitution Strategy
Let $u = (3x+1)^2 \geq 0$, the original equation becomes:
$$e^u + u = e^1 + 1$$
Key is identifying the repeating structure in the composite function
🔧 Solution Steps
  1. Structure Recognition: Observe $(3x+1)^2$ appears repeatedly
  2. Substitution Simplification: Let $u = (3x+1)^2$, equation becomes $e^u + u = e + 1$
  3. Monotonicity Analysis: $f(u) = e^u + u$, $f'(u) = e^u + 1 > 0$
  4. Solution Determination: $f(u) = f(1) \Rightarrow u = 1$
  5. Back-Substitute: $(3x+1)^2 = 1 \Rightarrow 3x+1 = \pm 1$
  6. Final Answer: $x = 0$ or $x = -\frac{2}{3}$
⚠️ Common Mistakes
  • Incomplete Substitution: Not fully identifying repeated composite structure
  • Forgetting Square Root: From $(3x+1)^2 = 1$, forgetting to consider $\pm 1$ cases
  • Monotonicity Judgment: Not using function monotonicity to ensure solution uniqueness

10. Problem 10: Polar Coordinates Basics

🎯 Intuitive Understanding

Polar coordinate system uses distance and angle to describe point positions, where $r$ directly represents the distance from point to origin - this is the core concept of polar coordinates.

Basic Definition
For polar coordinate point $P(r, \theta)$:
$$|OP| = |r|$$
$r$'s sign determines whether point is in direction $\theta$ or $\theta + \pi$
🔧 Sign Convention
  • $r > 0$: Point is in direction of angle $\theta$
  • $r < 0$: Point is in direction of angle $\theta + \pi$ (opposite direction)
  • $r = 0$: Point is at origin, angle is meaningless
⚠️ Common Mistakes
  • Sign Confusion: Thinking $r$ must be positive
  • Distance Concept: Confusing $r$ with distance to coordinate axes
  • Direction Understanding: Not understanding geometric meaning of negative $r$

Problem 11: Plane Side Determination

Notion Q11
Question

How to determine if points $P_1, P_3$ are on the same side of plane $\Pi$?

Method
  1. Calculate Dot Products: $$d_1 = \overrightarrow{AP_1} \cdot \boldsymbol{n}, \quad d_3 = \overrightarrow{AP_3} \cdot \boldsymbol{n}$$
  2. Compare Signs:
    • $\text{sgn}(d_1) = \text{sgn}(d_3)$ → Same side
    • $\text{sgn}(d_1) \neq \text{sgn}(d_3)$ → Different sides
  3. Special Case: If a dot product is 0, that point is on the plane
Answer: Compare dot product signs
Geometric Intuition

The sign of the dot product reflects the angle between the vector and normal vector: vectors from points on the same side form angles with the normal that are both either acute or obtuse.

Problem 12: Gauss-Jordan Elimination

Notion Q12
Question

What matrix does Gauss-Jordan elimination aim to produce?

RREF Definition

Goal: Reduced Row Echelon Form (RREF)

RREF Characteristics:
  1. First non-zero element in each non-zero row (pivot) is 1
  2. All other elements in pivot columns are 0
  3. Pivot positions strictly increase to the right in each row
  4. All-zero rows are at the bottom of matrix
Answer: Reduced Row Echelon Form (RREF)

Problem 13: Transpose of Matrix Inverse

Notion Q13
Question

What is $(A^T)^{-1}$ equal to?

Important Property Proof

Conclusion:$(A^T)^{-1} = (A^{-1})^T$

Proof:
  1. Start from $AA^{-1} = I$
  2. Take transpose of both sides: $(AA^{-1})^T = I^T$
  3. Use transpose property: $(A^{-1})^T (A^T) = I$
  4. By uniqueness of inverse: $(A^T)^{-1} = (A^{-1})^T$
Answer: $(A^{-1})^T$
Memory Trick

"Transpose of inverse = Inverse of transpose", operation order can be swapped.

Problem 14: System Consistency

Notion Q14
Question

What appears in the row echelon form of the augmented matrix as necessary and sufficient condition for linear system $Ax = b$ to have no solution?

No-Solution Characteristic

Key Row Pattern: $[0 \quad 0 \quad \cdots \quad 0 \mid c \neq 0]$

Explanation:
  • All zeros on left side means no variables
  • Non-zero on right side means equation is $0 = c$ (contradiction)
  • Such equation clearly has no solution
Answer: A row of form $[0 \cdots 0 \mid c \neq 0]$

Problem 15: Geometric Interpretation

Notion Q15
Question

What is the geometric interpretation of equation $x + 2y + 4z = 45$ in 3D space?

Geometric Meaning

Answer: A plane

General Understanding:
  • Equation of form $ax + by + cz = d$ represents a plane in 3D space
  • Coefficients $(a,b,c)$ form the plane's normal vector
  • Constant $d$ determines plane's distance from origin
Analysis:
  • Normal vector: $\boldsymbol{n} = (1, 2, 4)$
  • This plane doesn't pass through origin ($d = 45 \neq 0$)

📝 Update Log

August 13, 2025 Sync Update
  • Topics 8-15 Completed: Updated questions, solutions, and common mistakes according to latest Notion version.
  • Enhanced Visualization: Added SVG diagrams covering matrix multiplication, function analysis, polar coordinates, vector planes, etc.
🎯 Topic Coverage Checklist
✅ Matrix Multiplication
✅ Composite Function Differentiation
✅ Polar Coordinate System
✅ Vectors & Planes
✅ Determinant Properties
✅ Linear System Solutions
✅ RREF Construction
✅ Invertibility Criteria
✅ Fundamental Theorem of Calculus
✅ Geometric Interpretation
✅ IVT Application
✅ Parametric Equation Discussion
📚 Study Suggestions
  1. Combined Practice: Use these notes alongside the 50-question quiz
  2. Conceptual Diagrams: Focus on SVG diagrams to build geometric intuition
  3. Common Mistakes: Pay special attention to "Common Mistakes" sections
  4. Formula Memory: Use terminology mapping table to reinforce English-Chinese correspondence