考研公式大全-提问版-数学二

    科技2025-12-02  30

    完整版

    https://blog.csdn.net/zhaohongfei_358/article/details/106039576

    基础回顾

    面(体)积公式

    球 表 面 积 公 式 : S = 球 体 积 公 式 : V = 圆 锥 体 积 公 式 : V = 椭 圆 面 积 公 式 : S = 扇 形 面 积 公 式 : S =       ( 其 中 l 为 弧 长 , r 为 半 径 , θ 为 夹 角 ( 用 π 表 示 ) ) \begin{aligned} & \\ & 球表面积公式:S= \\ \\ & 球体积公式:V = \\ \\ & 圆锥体积公式:V= \\\\ & 椭圆面积公式: S= \\ \\ & 扇形面积公式: S= ~~~~~(其中l为弧长,r为半径,\theta为夹角(用\pi表示)) \end{aligned} S=V=V=S=S=     lrθ(π)

    一元二次方程基础

    一 元 二 次 方 程 : 根 的 公 式 :      韦 达 定 理 : x 1 + x 2 =         x 1 x 2 = 判 别 式 : Δ = b 2 − 4 a c    ⟹    { Δ > 0 Δ = 0 Δ < 0 抛 物 线   y = a x 2 + b x + c 的 顶 点 : ( ) \begin{aligned} & \\ & 一元二次方程: \\ \\ & 根的公式: ~~~~ \\ \\ & 韦达定理: x_1 + x_2 = ~~~~~~~ x_1 x_2 = \\ \\ & 判别式: \Delta=b^2 - 4ac \implies \begin{cases} \Delta >0 \\ \Delta =0 \\ \Delta <0 \\ \end{cases} \\\\ & 抛物线~ y=ax^2 + bx + c 的顶点:() \end{aligned}     x1+x2=       x1x2=Δ=b24acΔ>0Δ=0Δ<0线 y=ax2+bx+c()

    极坐标方程与直角坐标转换

    直 角 坐 标 化 极 坐 标 { x = y =    ⟹    x 2 + y 2 = 极 坐 标 化 直 角 坐 标 : ρ 2 = x 2 + y 2    ⟹    tan ⁡ θ = \begin{aligned} &直角坐标化极坐标 \begin{cases} x = \\ y = \end{cases} \implies x^2+y^2= \\\\ &极坐标化直角坐标 :\rho ^2 = x^2+y^2 \implies \tan \theta = \\\\ \end{aligned} {x=y=x2+y2=ρ2=x2+y2tanθ=

    切线与法线方程

    切 线 方 程 : 法 线 方 程 : \begin{aligned} & 切线方程: \\ \\ & 法线方程: \end{aligned} 线线

    因式分解公式

    ( a + b ) 2 = ( a − b ) 2 = ( a + b ) 3 = ( a − b ) 3 = ( a + b ) ( a − b ) = a 3 + b 3 = a 3 − b 3 = a n − b n = ( a + b ) n = \begin{aligned} & \\ & (a+b)^2 = \\ \\ & (a-b)^2 = \\ \\ & (a+b)^3 = \\ \\ & (a-b)^3 = \\ \\ & (a+b)(a-b) = \\ \\ & a^3 + b^3 = \\ \\ & a^3-b^3 = \\ \\ & a^n-b^n = \\ \\ & (a+b)^n = \end{aligned} (a+b)2=(ab)2=(a+b)3=(ab)3=(a+b)(ab)=a3+b3=a3b3=anbn=(a+b)n=

    阶乘与双阶乘

    n ! =       ( 规 定 0 ! = ? ) ( 2 n ) ! ! = ( 2 n − 1 ) ! ! = \begin{aligned} & n! = ~~~~~(规定0!=?) \\ \\ & (2n)!! = \\ \\ & (2n-1)!! = \end{aligned} n!=     (0!=)(2n)!!=(2n1)!!=

    函数的奇偶性

    定 义 在 [ − a , a ] 上 的 任 一 函 数 , 可 以 表 示 为 一 个 奇 函 数 与 一 个 偶 函 数 之 和 : f ( x ) = \begin{aligned} & 定义在[-a,a]上的任一函数,可以表示为一个奇函数与一个偶函数之和: \\ \\ & f(x) = \end{aligned} [a,a]f(x)=

    排列组合

    A n m = C n m = \begin{aligned} A_n^m & = \\\\ C_n^m & = \end{aligned} AnmCnm==

    等差数列

    a n = S n = S n = \begin{aligned} & a_n = \\ \\ & S_n = \\ \\ & S_n = \\ \\ \end{aligned} an=Sn=Sn=

    等比数列

    a n = S n = \begin{aligned} & a_n = \\ \\ & S_n = \end{aligned} an=Sn=

    常用数列前n项和

    ∑ k = 1 n k = 1 + 2 + 3 + ⋯ + n = ∑ k = 1 n ( 2 k − 1 ) = 1 + 3 + 5 + ⋯ + ( 2 n − 1 ) = ∑ k = 1 n k 2 = 1 2 + 2 2 + 3 2 + ⋯ + n 2 = ∑ k = 1 n k 3 = 1 3 + 2 3 + 3 3 + ⋯ + n 3 = ∑ k = 1 n k ( k + 1 ) = 1 × 2 + 2 × 3 + 3 × 4 + ⋯ + n ( n + 1 ) = ∑ k = 1 n 1 k ( k + 1 ) = 1 1 × 2 + 1 2 × 3 + 1 3 × 4 + ⋯ + 1 n ( n + 1 ) = \begin{aligned} & \\ & \sum_{k=1}^n k = 1 + 2+3+\cdots + n= \\ \\ & \sum_{k=1}^n (2k-1) = 1+ 3 + 5 + \cdots + (2n-1) = \\ \\ & \sum_{k=1}^n k^2 = 1^2+2^2+3^2+\cdots +n^2 = \\ \\ & \sum_{k=1}^n k^3 = 1^3 + 2^3 +3^3 +\cdots + n^3 = \\ \\ & \sum_{k=1}^n k(k+1) = 1 \times 2 + 2 \times 3 + 3 \times 4 + \cdots + n(n+1) = \\\\ & \sum_{k=1}^n \frac{1}{k(k+1)} = \frac{1}{1 \times 2} + \frac{1}{2 \times 3} + \frac{1}{3 \times 4} + \cdots + \frac{1}{n(n+1)} = \end{aligned} k=1nk=1+2+3++n=k=1n(2k1)=1+3+5++(2n1)=k=1nk2=12+22+32++n2=k=1nk3=13+23+33++n3=k=1nk(k+1)=1×2+2×3+3×4++n(n+1)=k=1nk(k+1)1=1×21+2×31+3×41++n(n+1)1=

    不等式

    ? ≤ a 2 + b 2 ? ≤ ∣ a ∣ + ∣ b ∣ ? ≤ ∣ a − b ∣ ? ≤ ∣ a 1 ∣ + ∣ a 2 ∣ + ⋅ ⋅ ⋅ + ∣ a n ∣ ? ≤ ∫ a b ∣ f ( x ) ∣ d x       ( a < b ) a b     ?     a + b 2     ?     a 2 + b 2 2       ( a , b > 0 ) a b c 3     ?     a + b + c 3     ?     a 2 + b 2 + c 2 3       ( a , b , c > 0 )     ?     a 1 + a 2 + . . . + a n n     ?         ?     x p p + x q q ( a c + b d ) 2     ?     ( a 2 + b 2 ) ( c 2 + d 2 ) ( a 1 b 1 + a 2 b 2 + a 3 b 3 ) 2     ?     ( a 1 2 + a 2 2 + a 3 2 ) ( b 1 2 + b 2 2 + b 3 2 ) [ ∫ a b f ( x ) ⋅ g ( x ) d x ] 2 ≤ sin ⁡ x     ?     x     ?     tan ⁡ x       ( 0 < x < π 2 ) arctan ⁡ x     ?     x     ?     arcsin ⁡ x       ( 0 ≤ x ≤ 1 ) x + 1     ?     e x ln ⁡ x     ?     x − 1 ? < ln ⁡ ( 1 + 1 x ) < ?       ( x > 0 ) \begin{aligned} & \\ & ? \le a ^ 2 + b^2 \\ \\ & ? \le |a| + |b| \\ \\ & ? \le |a-b| \\ \\ & ? \le |a_1| + |a_2| + \cdot\cdot\cdot + |a_n| \\ \\ & ? \le \int_a^b |f(x)| dx ~~~~~(a<b) \\ \\ & \sqrt{ab} ~~~?~~~ \frac{a+b}{2} ~~~?~~~ \sqrt{\frac{a^2+b^2}{2}} ~~~~~(a,b>0) \\ \\ & \sqrt[3]{abc} ~~~?~~~ \frac{a+b+c}{3} ~~~?~~~ \sqrt{\frac{a^2+b^2+c^2}{3}} ~~~~~(a,b,c>0) \\ \\ & ~~~?~~~ \frac{a_1+a_2+...+a_n}{n} ~~~?~~~\\ \\ & ~~~?~~~ \frac{x^p}{p} + \frac{x^q}{q} \\ \\ & (ac+bd)^2 ~~~?~~~ (a^2+b^2)(c^2+d^2) \\ \\ & (a_1 b_1 + a_2 b_2 + a_3 b_3)^2 ~~~?~~~ ({a_1}^2 + {a_2}^2 + {a_3}^2)({b_1}^2 + {b_2}^2 + {b_3}^2) \\ \\ & [\int_a^b f(x)\cdot g(x) dx]^2 \le \\ \\ & \sin x ~~~?~~~x ~~~?~~~ \tan x ~~~~~(0<x<\frac{\pi}{2}) \\ \\ & \arctan x ~~~?~~~ x ~~~?~~~ \arcsin x ~~~~~(0\le x \le 1) \\ \\ & x+1 ~~~?~~~ e^x \\ \\ & \ln x ~~~?~~~ x-1 \\ \\ & ? < \ln (1+\frac{1}{x}) < ? ~~~~~(x>0) \end{aligned} ?a2+b2?a+b?ab?a1+a2++an?abf(x)dx     (a<b)ab    ?   2a+b   ?   2a2+b2      (a,b>0)3abc    ?   3a+b+c   ?   3a2+b2+c2      (a,b,c>0)   ?   na1+a2+...+an   ?      ?   pxp+qxq(ac+bd)2   ?   (a2+b2)(c2+d2)(a1b1+a2b2+a3b3)2   ?   (a12+a22+a32)(b12+b22+b32)[abf(x)g(x)dx]2sinx   ?   x   ?   tanx     (0<x<2π)arctanx   ?   x   ?   arcsinx     (0x1)x+1   ?   exlnx   ?   x1?<ln(1+x1)<?     (x>0)

    三角函数公式

    诱导公式

    sin ⁡ ( − α ) = cos ⁡ ( − α ) = sin ⁡ ( π 2 − α ) = cos ⁡ ( π 2 − α ) = sin ⁡ ( π 2 + α ) = cos ⁡ ( π 2 + α ) = sin ⁡ ( π − α ) = cos ⁡ ( π − α ) = sin ⁡ ( π + α ) = cos ⁡ ( π + α ) = \begin{aligned} & \sin (-\alpha) = \\ \\ & \cos (-\alpha) = \\ \\ & \sin (\frac{\pi}{2} - \alpha) = \\ \\ & \cos (\frac{\pi}{2} - \alpha) = \\ \\ & \sin (\frac{\pi}{2} + \alpha) = \\ \\ & \cos (\frac{\pi}{2} + \alpha) = \\ \\ & \sin (\pi - \alpha) = \\ \\ & \cos (\pi - \alpha) = \\ \\ & \sin (\pi + \alpha) = \\ \\ & \cos (\pi + \alpha) = \\ \\ \\ \end{aligned} sin(α)=cos(α)=sin(2πα)=cos(2πα)=sin(2π+α)=cos(2π+α)=sin(πα)=cos(πα)=sin(π+α)=cos(π+α)=

    平方关系

    1 + tan ⁡ 2 α = 1 + cot ⁡ 2 α = sin ⁡ 2 α + cos ⁡ 2 α = \begin{aligned} & \\ & 1 + \tan ^2 \alpha = \\ \\ & 1 + \cot^2 \alpha = \\ \\ & \sin^2 \alpha + \cos ^2 \alpha = \end{aligned} 1+tan2α=1+cot2α=sin2α+cos2α=

    两角和与差的三角函数

    sin ⁡ ( α + β ) = cos ⁡ ( α + β ) = sin ⁡ ( α − β ) = cos ⁡ ( α − β ) = tan ⁡ ( α + β ) = tan ⁡ ( α − β ) = \begin{aligned} & \sin (\alpha + \beta) = \\ \\ & \cos (\alpha + \beta) = \\ \\ & \sin (\alpha - \beta) = \\ \\ & \cos (\alpha - \beta) = \\ \\ & \tan (\alpha + \beta) = \\ \\ & \tan (\alpha - \beta) = \end{aligned} sin(α+β)=cos(α+β)=sin(αβ)=cos(αβ)=tan(α+β)=tan(αβ)=

    积化和差公式

    cos ⁡ α cos ⁡ β = cos ⁡ α sin ⁡ β = sin ⁡ α cos ⁡ β = sin ⁡ α sin ⁡ β = \begin{aligned} & \cos \alpha \cos \beta = \\ \\ & \cos \alpha \sin \beta = \\ \\ & \sin \alpha \cos \beta = \\ \\ & \sin \alpha \sin \beta = \end{aligned} cosαcosβ=cosαsinβ=sinαcosβ=sinαsinβ=

    和差化积公式

    sin ⁡ α + sin ⁡ β = sin ⁡ α − sin ⁡ β = cos ⁡ α + cos ⁡ β = cos ⁡ α − cos ⁡ β = \begin{aligned} & \sin \alpha + \sin \beta = \\ \\ & \sin \alpha - \sin \beta = \\ \\ & \cos \alpha + \cos \beta = \\ \\ & \cos \alpha - \cos \beta = \end{aligned} sinα+sinβ=sinαsinβ=cosα+cosβ=cosαcosβ=

    倍角公式

    sin ⁡ 2 α = cos ⁡ 2 α = sin ⁡ 3 α = cos ⁡ 3 α = sin ⁡ 2 α = cos ⁡ 2 α = tan ⁡ 2 α = cot ⁡ 2 α = \begin{aligned} & \sin 2\alpha = \\ \\ & \cos 2\alpha = \\ \\ & \sin 3\alpha = \\ \\ & \cos 3 \alpha = \\ \\ & \sin^2 \alpha = \\ \\ & \cos^2 \alpha = \\ \\ & \tan 2\alpha = \\ \\ & \cot 2\alpha = \end{aligned} sin2α=cos2α=sin3α=cos3α=sin2α=cos2α=tan2α=cot2α=

    半角公式

    sin ⁡ 2 α 2 = cos ⁡ 2 α 2 = sin ⁡ α 2 = cos ⁡ α 2 = tan ⁡ α 2 = cot ⁡ α 2 = \begin{aligned} & \sin^2 \frac{\alpha}{2} = \\ \\ & \cos^2 \frac{\alpha}{2} = \\ \\ & \sin \frac{\alpha}{2} = \\ \\ & \cos \frac{\alpha}{2} =\\ \\ & \tan \frac{\alpha}{2} = \\ \\ & \cot \frac{\alpha}{2} = \end{aligned} sin22α=cos22α=sin2α=cos2α=tan2α=cot2α=

    万能公式

    sin ⁡ α = cos ⁡ α = \begin{aligned} & \sin \alpha = \\ \\ & \cos \alpha = \end{aligned} sinα=cosα=

    其他公式

    1 + sin ⁡ α = 1 − sin ⁡ α = \begin{aligned} & 1 + \sin \alpha = \\ \\ & 1 - \sin \alpha = \end{aligned} 1+sinα=1sinα=

    反三角函数恒等式

    arcsin ⁡ x + arccos ⁡ x = arctan ⁡ x + a r c c o t   x = sin ⁡ ( arccos ⁡ x ) = cos ⁡ ( arcsin ⁡ x ) = sin ⁡ ( arcsin ⁡ x ) = arcsin ⁡ ( sin ⁡ x ) = cos ⁡ ( arccos ⁡ x ) = arccos ⁡ ( cos ⁡ x ) = arccos ⁡ ( − x ) = \begin{aligned} & \arcsin x + \arccos x = \\ \\ & \arctan x + arccot ~x= \\ \\ & \sin(\arccos x) = \\ \\ & \cos(\arcsin x) = \\ \\ & \sin(\arcsin x) = \\ \\ & \arcsin (\sin x) = \\ \\ & \cos (\arccos x) = \\ \\ & \arccos (\cos x) = \\ \\ & \arccos (-x) = \end{aligned} arcsinx+arccosx=arctanx+arccot x=sin(arccosx)=cos(arcsinx)=sin(arcsinx)=arcsin(sinx)=cos(arccosx)=arccos(cosx)=arccos(x)=

    极限相关公式

    数列极限递推式

    a n + 1 = f ( a n ) 结 论 一 : f ′ ( x ) > 0 , { a 2 > a 1    ⟹    { a n } 单 调 递 ? a 2 < a 1    ⟹    { a n } 单 调 递 ? 结 论 二 ( 压 缩 映 像 原 理 ) : ? \begin{aligned} & a_{n+1} = f(a_n) \\\\ 结论一: & f'(x) > 0 , \begin{cases} a_2 > a_1 \implies \{ a_n \} 单调递? \\ a_2 < a_1 \implies \{ a_n \} 单调递? \end{cases} \\\\ 结论二(压缩映像原理):? \end{aligned} an+1=f(an)f(x)>0{a2>a1{an}a2<a1{an}

    重要极限公式

    lim ⁡ x → 0 + x α ln ⁡ x = lim ⁡ x → 0 + x α ( ln ⁡ x ) k = lim ⁡ x → + ∞ x α e − δ x = lim ⁡ x → 0 sin ⁡ x x = lim ⁡ x → 0 ( 1 + x ) 1 x = lim ⁡ n → ∞ n n = lim ⁡ n → ∞ a n = \begin{aligned} & \lim_{x \to 0^+} x^\alpha \ln x = \\\\ & \lim_{x \to 0^+} x^\alpha (\ln x)^k = \\\\ & \lim_{x \to +\infty} x^\alpha e^{-\delta x} = \\\\ & \lim_{x\to 0} \frac{\sin x}{x} = \\ \\ & \lim_{x \to 0} (1+x)^{\frac{1}{x}} = \\\\ & \lim_{n \to \infty} \sqrt[n]{n} = \\\\ & \lim_{n \to \infty} \sqrt[n]{a} = \\\\ \end{aligned} x0+limxαlnx=x0+limxα(lnx)k=x+limxαeδx=x0limxsinx=x0lim(1+x)x1=nlimnn =nlimna =

    常用等价无穷小

    x → 0 时 , x ∼ ? ? ? ? ? ? ?    ,    1 − cos ⁡ x ∼ ?    , ( 1 + x ) a − 1 ∼ ?    ,    a x − 1 ∼ ? \begin{aligned} & x \to 0 时,\\\\ & x \sim??????? ~~,~~ 1- \cos x \sim ? ~~, \\ \\ & (1+x)^a - 1 \sim ? ~~,~~ a^x - 1 \sim ? \end{aligned} x0x???????  ,  1cosx?  ,(1+x)a1?  ,  ax1?

    1^∞ 型

    lim ⁡ u v = e ? \lim u^v = e^? limuv=e?

    导数相关公式

    导数定义

    f ′ ( x 0 ) = f ′ ( x 0 ) = \begin{aligned} & f'(x_0) = \\\\ & f'(x_0) = \end{aligned} f(x0)=f(x0)=

    微分定义

    Δ y = Δ y = A Δ x = \begin{aligned} & \Delta y = \\ \\ & \Delta y = \\ \\ & A\Delta x = \end{aligned} Δy=Δy=AΔx=

    连续,可导及可微关系

    一元函数

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    多元函数

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    导数四则运算

    [ u ( x ) ± v ( x ) ] ′ = [ u ( x ) v ( x ) ] ′ = [ u ( x ) v ( x ) w ( x ) ] ′ = [ u ( x ) v ( x ) ] ′ = \begin{aligned} & [u(x) \pm v(x)]' = \\ \\ & [u(x)v(x)]' = \\ \\ & [u(x)v(x)w(x)]' = \\ \\ & \begin{bmatrix}\frac{u(x)}{v(x)} \end{bmatrix}' = \\ \\ \end{aligned} [u(x)±v(x)]=[u(x)v(x)]=[u(x)v(x)w(x)]=[v(x)u(x)]=

    复合函数求导

    { f [ g ( x ) ] } ′ = \{ f[g(x)] \}' = {f[g(x)]}=

    反函数求导

    y = f ( x ) , x = φ ( y )    ⟹    φ ′ ( y ) = y x ′ = y x x ′ ′ = \begin{aligned} & y = f(x), x = \varphi(y) \implies \varphi ' (y) = \\ \\ & y'_x = \\ \\ & y^{''}_{xx} = \end{aligned} y=f(x),x=φ(y)φ(y)=yx=yxx=

    参数方程求导

    { x = φ ( t ) y = ψ ( t ) d y d x = d 2 y d x 2 = \begin{aligned} & \begin{cases} x = \varphi (t) \\ y = \psi (t) \end{cases} \\\\ & \frac{dy}{dx} = \\ \\ & \frac{d^2 y}{dx^2} = \end{aligned} {x=φ(t)y=ψ(t)dxdy=dx2d2y=

    变限积分求导公式

    设 F ( x ) = ∫ φ 1 ( x ) φ 2 ( x ) f ( t ) d t , 则 F ′ ( x ) = \begin{aligned} & 设 F(x) = \int ^{\varphi_2(x)}_{\varphi_1(x)} f(t) dt, 则 \\ \\ & F'(x) = \end{aligned} F(x)=φ1(x)φ2(x)f(t)dt,F(x)=

    基本初等函数的导数公式(❤❤❤)

    ( x a ) ′ = ( a x ) ′ = ( e x ) ′ = ( l o g a x ) ′ = ( ln ⁡ x ) ′ = ( sin ⁡ x ) ′ = ( cos ⁡ x ) ′ = ( arcsin ⁡ x ) ′ = ( arccos ⁡ x ) ′ = ( tan ⁡ x ) ′ = ( cot ⁡ x ) ′ = ( arctan ⁡ x ) ′ = ( a r c c o t   x ) ′ = ( sec ⁡ x ) ′ = ( csc ⁡ x ) ′ = [ ln ⁡ ( x + x 2 + 1 ) ] ′ = [ ln ⁡ ( x + x 2 − 1 ) ] ′ = \begin{aligned} & (x^a)' = \\ \\ & (a^x)' = \\ \\ & (e^x)' = \\ \\ & (log_a x)' = \\ \\ & (\ln x)' = \\ \\ & (\sin x)' = \\ \\ & (\cos x)' = \\ \\ & (\arcsin x)' = \\ \\ & (\arccos x)' = \\ \\ & (\tan x)' = \\ \\ & (\cot x)' = \\ \\ & (\arctan x)' = \\ \\ & (arccot ~ x)' = \\ \\ & (\sec x)' = \\ \\ & (\csc x)' = \\ \\ & [\ln (x+\sqrt{x^2+1})]' = \\ \\ & [\ln (x+\sqrt{x^2-1})]' = \\ \\ \end{aligned} (xa)=(ax)=(ex)=(logax)=(lnx)=(sinx)=(cosx)=(arcsinx)=(arccosx)=(tanx)=(cotx)=(arctanx)=(arccot x)=(secx)=(cscx)=[ln(x+x2+1 )]=[ln(x+x21 )]=

    高阶导数的运算

    [ u ± v ] ( n ) = [u \pm v ]^{(n)} = [u±v](n)= ( u v ) ( n ) = \begin{aligned} (uv)^{(n)} & = \\ \end{aligned} (uv)(n)=

    常用初等函数的n阶导数公式

    ( a x ) ( n ) = ( e x ) ( n ) = ( sin ⁡ k x ) ( n ) = ( cos ⁡ k x ) ( n ) = ( ln ⁡ x ) ( n ) = [ ln ⁡ ( 1 + x ) ] ( n ) = [ ( x + x 0 ) m ] ( n ) = ( 1 x + a ) ( n ) = \begin{aligned} & (a^x)^{(n)} = \\ \\ & (e^x)^{(n)} = \\ \\ & (\sin kx)^{(n)} = \\ \\ & (\cos kx)^{(n)} = \\ \\ & (\ln x) ^ {(n)} = \\ \\ & [\ln(1+x)]^{(n)} = \\ \\ & [(x+x_0)^m]^{(n)} = \\ \\ & (\frac{1}{x+a})^{(n)} = \\ \\ \end{aligned} (ax)(n)=(ex)(n)=(sinkx)(n)=(coskx)(n)=(lnx)(n)=[ln(1+x)](n)=[(x+x0)m](n)=(x+a1)(n)=

    极值判别条件

    { 1.    f ′ ( x ) 左 右 异 号    ⟹    { 左 正 右 负    ⟹    极 ? 值 左 负 右 正    ⟹    极 ? 值 2.    f ′ ( x ) = 0 , f ′ ′ ( x ) ≠ 0    ⟹    { f ′ ′ ( x ) < 0    ⟹    极 ? 值 f ′ ′ ( x ) > 0    ⟹    极 ? 值 3.    f ′ ′ ( x ) 到 f ( n − 1 ) ( x ) = 0 , f ( n ) ( x ) ≠ 0 , n 为 ? 数    ⟹    { f ( n ) ( x ) < 0    ⟹    极 ? 值 f ( n ) ( x ) > 0    ⟹    极 ? 值 \begin{cases} 1.~~f'(x)左右异号 \implies \begin{cases} 左正右负 \implies 极?值 \\ 左负右正 \implies 极?值 \end{cases} \\ \\ 2.~~f'(x)=0, f''(x)\ne 0 \implies \begin{cases} f''(x) < 0 \implies 极?值 \\ f''(x)>0 \implies 极?值 \end{cases} \\ \\ 3. ~~f''(x) 到 f^{(n-1)}(x)=0 ,f^{(n)}(x) \ne 0, n为?数 \implies \begin{cases} f^{(n)}(x) < 0 \implies 极?值 \\ f^{(n)}(x) > 0 \implies 极?值 \end{cases} \end{cases} 1.  f(x){??2.  f(x)=0,f(x)=0{f(x)<0?f(x)>0?3.  f(x)f(n1)(x)=0f(n)(x)=0n?{f(n)(x)<0?f(n)(x)>0?

    凹凸性判定

    1. { f ( x 1 + x 2 2 ) < f ( x 1 ) + f ( x 2 ) 2    ⟹    ? f ( x 1 + x 2 2 ) > f ( x 1 ) + f ( x 2 ) 2    ⟹    ? 2. { f ′ ′ ( x ) > 0    ⟹    ? f ′ ′ ( x ) < 0    ⟹    ? \begin{aligned} 1.&\begin{cases} f(\frac{x_1+x_2}{2}) < \frac{f(x_1)+f(x_2)}{2} \implies ? \\\\ f(\frac{x_1+x_2}{2}) > \frac{f(x_1)+f(x_2)}{2} \implies ? \end{cases} \\\\ 2.&\begin{cases} f''(x) > 0 \implies ? \\\\ f''(x) < 0 \implies ? \end{cases} \end{aligned} 1.2.f(2x1+x2)<2f(x1)+f(x2)?f(2x1+x2)>2f(x1)+f(x2)?f(x)>0?f(x)<0?

    拐点判别条件

    { 1.    f ′ ′ ( x ) 左 右 异 号    ⟹    { 左 负 右 正    ⟹    ? 左 正 右 负    ⟹    ? 2.    f ′ ′ ( x ) = 0 , f ′ ′ ′ ( x ) ≠ 0    ⟹    { f ′ ′ ′ ( x ) < 0    ⟹    ? f ′ ′ ′ ( x ) > 0    ⟹    ? 3.    f ′ ′ ( x ) 到 f ( n − 1 ) ( x ) = 0 , f ( n ) ( x ) ≠ 0 , n 为 ? 数    ⟹    { f ( n ) ( x ) < 0    ⟹    ? f ( n ) ( x ) > 0    ⟹    ? \begin{cases} 1.~~f''(x)左右异号 \implies \begin{cases} 左负右正 \implies ? \\ 左正右负 \implies ? \end{cases} \\ \\ 2.~~f''(x)=0, f'''(x)\ne 0 \implies \begin{cases} f'''(x) < 0 \implies ? \\ f'''(x)>0 \implies ? \end{cases} \\ \\ 3. ~~f''(x) 到 f^{(n-1)}(x)=0 ,f^{(n)}(x) \ne 0, n为?数 \implies \begin{cases} f^{(n)}(x) < 0 \implies ? \\ f^{(n)}(x) > 0 \implies ? \end{cases} \end{cases} 1.  f(x){??2.  f(x)=0,f(x)=0{f(x)<0?f(x)>0?3.  f(x)f(n1)(x)=0f(n)(x)=0n?{f(n)(x)<0?f(n)(x)>0?

    斜渐近线

    lim ⁡ x → ? ? = a      lim ⁡ x → ? ( ? ) = b    ⟹    斜 渐 近 线 为 : y = a x + b \lim_{x \to ?} ? = a ~~~~\lim_{x \to ?}(?) = b \implies 斜渐近线为: y=ax+b x?lim?=a    x?lim(?)=b线y=ax+b

    曲率

    密 切 圆 半 径       r = ? 曲 率       K = ? 曲 率 圆       ? \begin{aligned} 密切圆半径 ~~~~~ & r = ? \\ \\ 曲率 ~~~~~ &K = ? \\ \\ 曲率圆 ~~~~~& ? \end{aligned}                r=?K=??

    积分相关公式

    定积分的精确定义

    ∫ a b f ( x ) d x = ? 常 用 : ∫ 0 1 f ( x ) d x = ? ∫ 0 k f ( x ) d x = ? 二 重 定 积 分 精 确 定 义 : ∬ D f ( x , y ) d σ = ? 常 用 : ∫ 0 1 ∫ 0 1 f ( x , y ) d x d y = ? \begin{aligned} & \int_a^b f(x) dx = ? \\ \\ \\ 常用:& \int_0^1 f(x) dx = ? \\ \\ & \int_0^k f(x) dx = ? \\ \\ \\ 二重定积分精确定义:& \iint\limits_D f(x,y) d\sigma = ? \\ \\ \\ 常用:&\int_0^1 \int_0^1 f(x,y) dxdy = ? \end{aligned} abf(x)dx=?01f(x)dx=?0kf(x)dx=?Df(x,y)dσ=?0101f(x,y)dxdy=?

    分布积分公式

    ∫ u d v = ? ∫ u v ( n + 1 ) d x = ? \begin{aligned} & \int u dv = ? \\ & \int uv^{(n+1)}dx = ? \end{aligned} udv=?uv(n+1)dx=?

    分部积分表格法

    ? ? ?

    区间再现公式

    ∫ a b f ( x ) d x = ? \int_a^b f(x) dx = ? abf(x)dx=?

    华里士公式

    ? ?

    敛散性判别公式

    ∫ 1 + ∞ 1 x p d x    ⟹    ? ∫ 0 1 1 x p d x    ⟹    ? \begin{aligned} & \int_1^{+\infty} \frac{1}{x^p} dx \implies ? \\\\ & \int_0^1 \frac{1}{x^p} dx \implies & ? \\\\ \end{aligned} 1+xp1dx?01xp1dx?

    基本积分公式

    以 下 公 式 中 , α 与 a 均 为 常 数 , 除 声 明 者 外 , a > 0 ∫ x α d x = ? ∫ 1 x d x = ? ∫ a x d x = ? ∫ e x d x = ? ∫ sin ⁡ x d x = ? ∫ cos ⁡ x d x = ? ∫ tan ⁡ x d x = ? ∫ cot ⁡ x d x = ? ∫ sec ⁡ x d x = ? ∫ csc ⁡ x d x = ? ∫ sec ⁡ 2 x d x = ? ∫ csc ⁡ 2 x d x = ? ∫ 1 a 2 + x 2 d x = ? ∫ 1 a 2 − x 2 d x = ? ∫ 1 a 2 − x 2 d x = ? ∫ 1 x 2 ± a 2 d x = ? \begin{aligned} & 以下公式中,\alpha 与 a 均为常数,除声明者外,a>0 \\ \\ & \int x^\alpha dx = ? \\ \\ & \int \frac{1}{x} dx =?\\ \\ & \int a^x dx =? \\ \\ & \int e^x dx = ? \\ \\ & \int \sin x dx =? \\ \\ & \int \cos x dx =? \\ \\ & \int \tan x dx = ? \\ \\ & \int \cot xdx =? \\ \\ & \int \sec x dx =? \\ \\ & \int \csc x dx =? \\ \\ & \int \sec ^2 x dx = ? \\ \\ & \int \csc^2x dx = ? \\ \\ & \int \frac{1}{a^2 + x^2} dx = ? \\ \\ & \int \frac{1}{a^2-x^2} dx =? \\ \\ & \int \frac{1}{\sqrt{a^2-x^2}} dx =? \\ \\ & \int \frac{1}{\sqrt{x^2 \pm a^2}} dx = ? \end{aligned} αaa>0xαdx=?x1dx=?axdx=?exdx=?sinxdx=?cosxdx=?tanxdx=?cotxdx=?secxdx=?cscxdx=?sec2xdx=?csc2xdx=?a2+x21dx=?a2x21dx=?a2x2 1dx=x2±a2 1dx=

    重要积分公式

    ∫ − ∞ + ∞ e − x 2 d x = ? = ? ∫ 0 + ∞ x n e − x d x = ? ∫ − a a f ( x ) d x = ∫ 0 a ? d x ∫ 0 π x f ( sin ⁡ x ) d x = ? = ? ∫ a b f ( x ) d x = ? ∫ 0 1 ? d x \begin{aligned} & \int_{-\infty}^{+\infty} e^{-x^2} dx = ? = ? \\ \\ & \int_{0}^{+\infty} x^n e^{-x} dx = ? \\ \\ & \int_{-a}^{a} f(x) dx = \int_0^a ?dx \\ \\ & \int_0^\pi xf(\sin x) dx = ?=? \\ \\ & \int_a^b f(x) dx = ? \int_0^1 ? dx \end{aligned} +ex2dx==0+xnexdx=aaf(x)dx=0adx0πxf(sinx)dx==abf(x)dx=01dx

    积分求平均值

    f ( x ) 在 [ a , b ] 上 的 平 均 值 为 : ? f(x) 在[a,b]上的平均值为: ? f(x)[a,b]

    定积分应用

    定积分求平面图形面积

    y = y 1 ( x ) 与 y = y 2 ( x ) , 及 x = a , x = b ( a < b ) 所 围 成 的 平 面 图 形 面 积 : S = ? 曲 线 r = r 1 ( θ ) 与 r = r 2 ( θ ) 与 两 射 线 θ = α 与 θ = β ( 0 < β − α ≤ 2 π ) 所 围 成 的 曲 边 扇 形 的 面 积 : S = ? \begin{aligned} & y=y_1(x) 与 y=y_2(x),及x=a,x=b(a<b)所围成的平面图形面积:\\ \\ & S= ? \\ \\ \\ & 曲线 r=r_1(\theta) 与 r=r_2(\theta) 与 两射线 \theta = \alpha 与 \theta = \beta (0<\beta - \alpha \le 2\pi)所围成的曲边扇形的面积:\\ \\ & S = ? \end{aligned} y=y1(x)y=y2(x)x=ax=ba<bS=线r=r1(θ)r=r2(θ)线θ=αθ=β0<βα2πS=

    定积分求旋转体的体积

    曲 线 y = y ( x ) 与 x = a , x = b ( a < b ) 及 x 轴 围 成 的 曲 边 梯 形 绕 x 轴 旋 转 一 周 所 得 到 的 旋 转 体 的 体 积 V = ? 曲 线 y = y 1 ( x ) ≥ 0 与 y = y 2 ( x ) ≥ 0 及 x = a , x = b ( a < b ) 所 围 成 的 平 面 图 形 绕 x 轴 旋 转 一 周 所 的 到 的 旋 转 体 的 体 积 V = ? 曲 线 y = y ( x ) 与 x = a , x = b ( 0 ≤ a < b ) 及 x 轴 围 成 的 曲 边 梯 形 绕 y 轴 旋 转 一 周 所 得 到 的 的 旋 转 体 的 体 积 V y = ? 曲 线 y = y 1 ( x ) 与 y = y 2 ( x ) 及 x = a , x = b ( 0 ≤ a ≤ b ) 所 围 成 的 圆 形 绕 y 轴 旋 转 一 周 所 成 的 旋 转 体 的 体 积 V = ? \begin{aligned} & 曲线 y=y(x)与x=a,x=b(a<b)及x轴围成的曲边梯形绕x轴旋转一周所得到的旋转体的体积 \\ \\ & V = ? \\ \\ \\ & 曲线y=y_1(x) \ge 0 与 y = y_2(x) \ge 0 及 x=a,x=b(a<b)所围成的平面图形绕x轴旋转一周所的到的旋转体的体积 \\ \\ & V = ? \\ \\ \\ & 曲线 y=y(x) 与 x=a,x=b(0\le a < b) 及x轴围成的曲边梯形绕y轴旋转一周所得到的的旋转体的体积 \\ \\ & V_y = ?\\ \\ \\ & 曲线y=y_1(x)与y=y_2(x)及x=a,x=b(0\le a \le b)所围成的圆形绕y轴旋转一周所成的旋转体的体积 \\\\ & V= ? \end{aligned} 线y=y(x)x=ax=b(a<b)xxV=线y=y1(x)0y=y2(x)0x=ax=ba<bxV=线y=y(x)x=a,x=b0a<bxyVy=线y=y1(x)y=y2(x)x=ax=b0abyV=

    平面曲线的弧长

    L = ∫ a b ? d x L = ∫ α β ? d t L = ∫ α β ? d θ \begin{aligned} & L = \int_a^b ?dx \\ \\ & L = \int_\alpha^\beta ?dt \\ \\ & L = \int_\alpha^\beta ?d\theta \end{aligned} L=abdxL=αβdtL=αβdθ

    旋转曲面的面积

    曲 线 y = y ( x ) 在 区 间 [ a , b ] 上 的 曲 线 弧 段 绕 x 轴 旋 转 一 周 所 得 到 的 旋 转 曲 面 的 “ 面 积 ” S = ? d x S = ? d t \begin{aligned} & 曲线y=y(x)在区间[a,b]上的曲线弧段绕x轴旋转一周所得到的旋转曲面的“面积” \\\\ & S = ?dx \\ \\ & S = ?dt \end{aligned} 线y=y(x)[a,b]线xS=dxS=dt

    平面截面面积为已知的立体体积

    在 区 间 [ a , b ] 上 , 垂 直 于 x 轴 的 平 面 截 立 体 Ω 所 得 到 截 面 面 积 为 x 的 连 续 函 数 A ( x ) , 则 Ω 的 体 积 为 V = ? \begin{aligned} & 在区间[a,b]上,垂直于x轴的平面截立体\Omega所得到截面面积为x的连续函数A(x),则\Omega的体积为 \\ \\ & V = ? \end{aligned} [a,b]xΩxA(x)ΩV=

    变力沿直线做功

    设 力 函 数 为 F ( x ) ( a ≤ x ≤ b ) , 则 物 体 沿 x 轴 从 点 a 移 动 到 点 b 时 , 变 力 F ( x ) 所 做 的 功 为 W = ? \begin{aligned} & 设力函数为F(x) (a\le x \le b),则物体沿x轴从点a移动到点b时,变力F(x)所做的功为 \\\\ & W = ? \end{aligned} F(x)axb沿xabF(x)W=

    抽水做功

    W = ?       \begin{aligned} & W = ? ~~~~~\\\\ \end{aligned} W=     

    水压力

    P = ?       \begin{aligned} & P = ? ~~~~~\\\\ \end{aligned} P=?     

    质心

    直线段的质心(一维)

    x ˉ = \begin{aligned} & \bar{x} = \end{aligned} xˉ=

    不均匀薄片质心(二维)

    x ˉ = y ˉ = \begin{aligned} & \bar{x} = \\\\ & \bar{y} = \\\\ \end{aligned} xˉ=yˉ=

    形心

    x ˉ = y ˉ = \begin{aligned} & \bar{x} = \\\\ & \bar{y} = \\\\ \end{aligned} xˉ=yˉ=

    质量

    m = ∬ D ? d x d y m = \iint\limits_D ?dxdy m=D?dxdy

    转动惯量

    I x = I y = \begin{aligned} & I_x = & I_y = \end{aligned} Ix=Iy=

    物理公式

    浮 力 公 式 :     F 浮 = 压 强 :     P = 压 强 与 气 体 体 积 成 反 比 :     水 深 h 处 的 压 强 :     P =   在 水 中 的 压 力 :     F 压 = 力 :     F = 做 功 :     W 功 = 引 力 :     F = \begin{aligned} 浮力公式:~~~ & F_{浮} = \\\\ 压强:~~~& P = \\\\ 压强与气体体积成反比:~~~& \\\\ 水深h处的压强:~~~& P=\ \\\\ 在水中的压力:~~~& F_{_压} = \\ \\ 力:~~~& F = \\ \\ 做功:~~~& W_功 = \\\\ 引力:~~~ & F = \end{aligned}          h               F=P=P= F=F=W=F=

    泰勒公式

    f ( x ) = \begin{aligned} f(x) & = \\ \\ \end{aligned} f(x)=

    拉格朗日余项的泰勒公式

    f ( x ) = f(x) = f(x)=

    佩亚诺余项的泰勒公式

    f ( x ) = f(x) = f(x)=

    常用的泰勒展开式

    sin ⁡ x = cos ⁡ x = arcsin ⁡ x = ( 1 + x ) α = 1 1 − x = 1 1 + x = ln ⁡ ( 1 + x ) = 1 1 + x 2 = arctan ⁡ x = tan ⁡ x = e x = \begin{aligned} & \sin x = \\ \\ & \cos x = \\ \\ & \arcsin x = \\ \\ \\ & (1+x)^\alpha =\\ \\ & \frac{1}{1-x} = \\ \\ & \frac{1}{1+x} = \\ \\ & \ln (1+x) = \\ \\ & \frac{1}{1+x^2} = \\ \\ & \arctan x = \\ \\ & \tan x = \\ \\ \\ & e^x = \end{aligned} sinx=cosx=arcsinx=(1+x)α=1x1=1+x1=ln(1+x)=1+x21=arctanx=tanx=ex=

    中值定理

    罗尔定理

    罗尔定理推论

    若 ? 至 多 ? 个 根    ⟹    至 多 ? 个 根 若?至多?个根 \implies 至多?个根 ???

    罗尔定理证明题辅助函数构造

    f ′ ′ ( x ) + g ( x ) f ′ ( x ) = 0    ⟹    F ( x ) = f ( x ) + g ( x ) ∫ 0 x f ( t ) d t = 0    ⟹    F ( x ) = f ′ ( x ) + g ( x ) [ f ( x ) − 1 ] = 0    ⟹    F ( x ) = ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ \begin{aligned} & f''(x) + g(x)f'(x) = 0 \implies F(x) = \\ \\ & f(x) + g(x)\int_0^x f(t)dt = 0 \implies F(x) = \\ \\ & f'(x) + g(x)[f(x)-1] =0 \implies F(x) = \\ \\ & \cdot\cdot\cdot\cdot\cdot\cdot\cdot\cdot \end{aligned} f(x)+g(x)f(x)=0F(x)=f(x)+g(x)0xf(t)dt=0F(x)=f(x)+g(x)[f(x)1]=0F(x)=

    拉格朗日中值定理

    柯西中值定理

    积分中值定理

    f ( x ) 在 [ a , b ] 上 连 续    ⟹    存 在 η ∈ [ a , b ] , 使 得 ∫ a b f ( x ) d x = f ( x ) , g ( x ) 在 [ a , b ] 上 连 续 , 且 g ( x ) 不 变 号    ⟹    ∫ a b f ( x ) g ( x ) d x = 二 重 积 分 中 值 定 理 , D 上 连 续 , A 为 D 的 面 积    ⟹    ∬ D f ( x , y ) d x d y = \begin{aligned} & f(x)在[a,b]上连续 \implies 存在 \eta \in [a,b], 使得 \\ & \int_a^b f(x) dx = \\ \\ \\ & f(x),g(x)在[a,b]上连续,且g(x)不变号 \implies \\ & \int_a^b f(x)g(x)dx = \\\\\\ & 二重积分中值定理,D上连续,A为D的面积 \implies \\ & \iint\limits_D f(x,y) dxdy = \end{aligned} f(x)[a,b]η[a,b],使abf(x)dx=f(x)g(x)[a,b]g(x)abf(x)g(x)dx=DADDf(x,y)dxdy=

    多元微积分相关公式

    多元微分定义

    定 义 : Δ z = 全 增 量 : Δ z = 线 性 增 量 : 可 微 判 定 : \begin{aligned} 定义:& \Delta z = \\ \\ 全增量:& \Delta z = \\ \\ 线性增量:& \\ \\ 可微判定:& \end{aligned} 线Δz=Δz=

    多元隐函数求导

    ∂ z ∂ x = ∂ z ∂ y = \begin{aligned} & \frac{\partial z}{\partial x} = \\ \\ & \frac{\partial z}{\partial y} = \\ \end{aligned} xz=yz=

    极坐标下二重积分计算法

    ∬ D f ( x , y ) d σ = \underset{D}{\iint} f(x,y)d\sigma = Df(x,y)dσ=

    隐函数存在定理

    ?

    多元函数极值判定

    ?

    拉格朗日数乘法求最值

    ?

    多重积分的应用

    空间曲面的面积

    A = ? \begin{aligned} & \\ & A = ? \end{aligned} A=?

    微分方程

    一阶线性微分方程

    y ′ + p ( x ) y = q ( x )       其 中 p ( x ) , q ( x ) 为 连 续 函 数    ⟹    \begin{aligned} & y' + p(x)y = q(x) ~~~~~其中p(x),q(x)为连续函数 \implies \\\\ \end{aligned} y+p(x)y=q(x)     p(x),q(x)

    二阶常系数齐次线性微分方程的通解

    y ′ ′ + p y ′ + q y = 0          p , q 为 常 数 ⇒ 特 征 方 程 为    λ 2 + p λ + q = 0    ⟹    ? \begin{aligned} & y'' + py' + qy =0 ~~~~~~~~p,q为常数 \\ \\ \xRightarrow{特征方程为} & ~~ \lambda^2 + p\lambda + q = 0 \implies ? \end{aligned} y+py+qy=0        p,q  λ2+pλ+q=0?

    三阶常系数齐次线性微分方程的通解

    y ′ ′ ′ + p y ′ ′ + q y ′ + r y = 0          p , q , r 为 常 数 ⇒ 特 征 方 程 为    λ 3 + p λ 2 + q λ + r = 0    ⟹    ? \begin{aligned} & y''' + py'' + qy' +ry =0 ~~~~~~~~p,q,r为常数 \\ \\ \xRightarrow{特征方程为} & ~~ \lambda^3 + p\lambda^2 + q\lambda + r = 0 \implies ? \end{aligned} y+py+qy+ry=0        p,q,r  λ3+pλ2+qλ+r=0?

    二阶常系数非齐次线性微分方程的特解

    y ′ ′ + p y ′ + q y = f ( x ) ( 1 ) 自 由 项 f ( x ) = P n ( x ) e a x   时 , 特 解 为   y ∗ = ? ( 2 ) 自 由 项   f ( x ) = e a x [ P m ( x ) cos ⁡ β x + P n ( x ) sin ⁡ β x ]   时 , y ∗ = ? \begin{aligned} & y'' + py'+qy = f(x) \\ \\ (1)& 自由项f(x)=P_n(x)e^{ax} ~时,特解为 ~ y^*= ? \\ \\ \\ \\ \\ (2) & 自由项~ f(x) = e^{ax}[P_m(x) \cos \beta x + P_n(x) \sin \beta x] ~时, \\ \\ & y^* = ? \end{aligned} 1(2)y+py+qy=f(x)f(x)=Pn(x)eax  y=? f(x)=eax[Pm(x)cosβx+Pn(x)sinβx] y=?

    “算子法”求二阶常系数非齐次线性微分方程的特解

    ?

    线性代数

    行列式

    ∣ A ∣ = ∣ A T ∣ ∣ k A ∣ = ? ∣ A B ∣ = ∣ A ∣ ∣ B ∣ A i j = ? M i j \begin{aligned} & |A| = |A^T| \\ \\ & |kA| = ? \\ \\ & |AB| = |A||B| \\ \\ & A_{ij} = ?M_{ij} \\ \\ \end{aligned} A=ATkA=?AB=ABAij=?Mij

    几个重要的行列式

    ∣ a 11 0 … 0 0 a 22 … 0 ⋮ ⋮ ⋮ 0 0 … a n n ∣ = ∣ a 11 0 … 0 a 21 a 22 … 0 ⋮ ⋮ ⋮ a n 1 a n 2 … a n n ∣ = ∣ a 11 a 12 … a 1 n 0 a 22 … a 2 n ⋮ ⋮ ⋮ 0 0 … a n n ∣ = ? ∣ 0 … 0 a 1 n 0 … a 2 , n − 1 0 ⋮ ⋮ ⋮ a n 1 … 0 0 ∣ = ∣ 0 … 0 a 1 n 0 … a 2 , n − 1 ⋮ a n 1 ∣ = ∣ a 1 n a 2 , n − 1 0 ⋮ ⋮ a n 1 … 0 0 ∣ = ? ∣ 1 1 ⋯ 1 x 1 x 2 ⋯ x n x 1 2 x 2 2 ⋯ x n 2 ⋮ ⋮ ⋮ x 1 n − 1 x 2 n − 1 ⋯ x n n − 1 ∣ = ? ∣ a b b ⋯ b b a b ⋯ b b b a ⋯ b ⋮ ⋮ ⋮ ⋮ b b b ⋯ a ∣ = ? ∣ A O O B ∣ = ∣ A C O B ∣ = ∣ A O C B ∣ = ∣ O A n × n B m × m O ∣ = ∣ O A B C ∣ = ∣ C A B O ∣ = \begin{aligned} & \begin{vmatrix} a_{11} & 0 & \dots & 0 \\ 0 & a_{22} & \dots & 0 \\ \vdots & \vdots & & \vdots \\ 0 & 0 & \dots & a_{nn} \end{vmatrix} = \begin{vmatrix} a_{11} & 0 & \dots & 0 \\ a_{21} & a_{22} & \dots & 0 \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \dots & a_{nn} \end{vmatrix} = \begin{vmatrix} a_{11} & a_{12} & \dots & a_{1n} \\ 0 & a_{22} & \dots & a_{2n} \\ \vdots & \vdots & & \vdots \\ 0 & 0 & \dots & a_{nn} \end{vmatrix} = ? \\ \\ \\ & \begin{vmatrix} 0 & \dots & 0 & a_{1n} \\ 0 & \dots & a_{2,n-1} & 0 \\ \vdots & & \vdots & \vdots \\ a_{n1} & \dots & 0 & 0 \end{vmatrix} = \begin{vmatrix} 0 & \dots & 0 & a_{1n} \\ 0 & \dots & a_{2,n-1} & \\ \vdots & & & \\ a_{n1} & & & \end{vmatrix} = \begin{vmatrix} & & & a_{1n} \\ & & a_{2,n-1} & 0 \\ & & \vdots & \vdots \\ a_{n1} & \dots & 0 & 0 \end{vmatrix} = ? \\ \\ \\ & \begin{vmatrix} 1 & 1 & \cdots & 1 \\ x_1 & x_2 & \cdots & x_n \\ x_1^2 & x_2^2 & \cdots & x_n^2 \\ \vdots & \vdots & & \vdots \\ x_1^{n-1} & x_2^{n-1} & \cdots & x_n^{n-1} \\ \end{vmatrix} = ? \\ \\ \\ & \begin{vmatrix} a & b & b & \cdots & b \\ b & a & b & \cdots & b \\ b & b & a & \cdots & b \\ \vdots & \vdots & \vdots & & \vdots \\ b & b & b & \cdots & a \\ \end{vmatrix} = ? \\ \\ \\ & \begin{vmatrix} A & O \\ O & B \end{vmatrix} = \begin{vmatrix} A & C \\ O & B \end{vmatrix} = \begin{vmatrix} A & O \\ C & B \end{vmatrix} = \\ \\ \\ & \begin{vmatrix} O & A_{n\times n} \\ B_{m\times m} & O \end{vmatrix} = \begin{vmatrix} O & A \\ B & C \end{vmatrix} = \begin{vmatrix} C & A \\ B & O \end{vmatrix} = \end{aligned} a11000a22000ann=a11a21an10a22an200ann=a1100a12a220a1na2nann=?00an10a2,n10a1n00=00an10a2,n1a1n=an1a2,n10a1n00=?1x1x12x1n11x2x22x2n11xnxn2xnn1=?abbbbabbbbabbbba=?AOOB=AOCB=ACOB=OBm×mAn×nO=OBAC=CBAO=

    矩阵

    ( A T ) T = ( k A ) T = ( A + B ) T = ( A B ) T = A A ∗ = A ∗ A = ∣ A ∗ ∣ = A − 1 = = ∣ A ∣ ( A ∗ ) − 1 ( A − 1 ) − 1 = ( k A ) − 1 = ( A B ) − 1 = ( A T ) − 1 = ( A T ) ∗ = ( A − 1 ) ∗ = ( A B ) ∗ = ( A ∗ ) ∗ = ∣ A − 1 ∣ = [ A ∣ E ] → 初 等 行 变 换 [ E ∣ A − 1 ] [ A ∣ B ] → 初 等 行 变 换 [ E ∣ ? ] \begin{aligned} & (A^T)^T = \\ \\ & (kA)^T = \\ \\ & (A+B)^T = \\ \\ & (AB)^T = \\ \\ & AA^* = A^*A = \\ \\ & |A^*|= \\ \\ & A^{-1} = \\ \\ & = |A|(A^*)^{-1} \\ \\ & (A^{-1})^{-1} = \\ \\ & (kA)^{-1} = \\ \\ & (AB)^{-1} = \\ \\ & (A^T)^{-1} = \\ \\ & (A^T)^* = \\ \\ & (A^{-1})^* = \\ \\ & (AB)^* = \\ \\ & (A^*)^* = \\ \\ & |A^{-1}| = \\ \\ & [A|E] \xrightarrow{初等行变换}[E|A^{-1}] \\\\ & [A|B] \xrightarrow{初等行变换}[E|?] \\\\ \end{aligned} (AT)T=(kA)T=(A+B)T=(AB)T=AA=AA=A=A1==A(A)1(A1)1=(kA)1=(AB)1=(AT)1=(AT)=(A1)=(AB)=(A)=A1=[AE] [EA1][AB] [E?]

    分块矩阵

    [ A O O B ] n = \begin{aligned} \begin{bmatrix} A & O \\ O & B \end{bmatrix}^n = \end{aligned} [AOOB]n=

    正交矩阵

    A 是 正 交 矩 阵    ⟺       ⟺       ⟺       ⟹    ∣ A ∣ =    ⟹    λ = \begin{aligned} & A是正交矩阵 \\\\ \iff & \\ \\ \iff &\\ \\ \iff & \\ \\ \implies & |A| = \\ \\ \implies & \lambda = \\ \\ \end{aligned} AA=λ=

    施密特正交化

    β 1 = β 2 = β 3 = ⋯ ⋯ β n = \begin{aligned} & \beta_1 = \\ \\ & \beta_2 = \\ \\ & \beta_3 =\\ \\ & \cdots\cdots \\\\ & \beta_n = \\ \\ \\ \end{aligned} β1=β2=β3=βn=

    可逆矩阵

    A 可 逆    ⟺       ⟺       ⟺    A x = 0    ⟺    A x = b    ⟺    r ( A ) =    ⟺    特 征 值 \begin{aligned} & A可逆 \\\\ \iff & \\ \\ \iff & \\\\ \iff & Ax=0 \\\\ \iff & Ax=b \\\\ \iff & r(A)= \\\\ \iff & 特征值 \end{aligned} AAx=0Ax=br(A)=

    等价矩阵

    A , B 等 价    ⟺    A ≅ B    ⟺       ⟺    { α 1 , α 2 , ⋯   , α s } ≅ { β 1 , β 2 , ⋯   , β t }    ⟺       ⟺       ⟺    \begin{aligned} & A, B等价 \\\\ \iff & A \cong B \\ \\ \iff & \\ \\ \iff & \\\\\\ & \{\alpha_1,\alpha_2, \cdots, \alpha_s\} \cong \{\beta_1,\beta_2, \cdots, \beta_t \} \\ \\ \iff & \\ \\ \iff & \\ \\ \iff & \\ \\ \end{aligned} A,BAB{α1,α2,,αs}{β1,β2,,βt}

    秩相关公式

    A 是 m × n 矩 阵 , 则 : r ( A ) ≤ m i n { m , n } r ( A T ) = r ( k A ) = r ( A + B ) ≤ r ( A B ) ≤ r ( A ) + r ( B ) − ? ≤ r ( A ) + r ( B ) ≤ ?      ( 其 中 , A B = O , n 是 A 的 列 数 或 B 的 行 数 ) r ( A O O B ) = ? ≤ r ( A O C B ) ≤ ? ? = r ( A T A ) r ( A ) = 1    ⟹    ∃ 非 零 α , β , 使 得 A = ? r ( α α T ) = ? { r ( A ) = ?        ⟹        r ( A ∗ ) = ? r ( A ) = ?        ⟹        r ( A ∗ ) = ? r ( A ) < ?        ⟹        r ( A ∗ ) = ? \begin{aligned} & A是m\times n矩阵,则:\\\\ & r(A) \le min\{m,n\} \\ \\ & r(A^T) = \\ \\ & r(kA) = \\ \\ & r(A+B) \le \\ \\ & r(AB) \le \\ \\ & r(A) + r(B) -? \le \\ \\ & r(A) + r(B) \le ? ~~~~(其中,AB=O,n是A的列数或B的行数) \\\\ & r\begin{pmatrix} A & O \\ O & B \end{pmatrix} =\\ \\ & ?\le r\begin{pmatrix} A & O \\ C & B \end{pmatrix} \le ? \\\\ & ?= r(A^T A) \\ \\ & r(A) = 1 \implies \exists 非零 \alpha, \beta ,使得 A = ? \\ \\ & r(\alpha \alpha^T) = ? \\ \\ & \begin{cases} r(A) = ? ~~~ \implies ~~~ r(A^*) = ? \\ r(A) = ? ~~~ \implies ~~~ r(A^*) = ? \\ r(A) < ? ~~~ \implies ~~~ r(A^*) = ? \\ \end{cases} \\\\ \end{aligned} Am×nr(A)min{m,n}r(AT)=r(kA)=r(A+B)r(AB)r(A)+r(B)?r(A)+r(B)    AB=O,nABr(AOOB)=r(ACOB)=r(ATA)r(A)=1α,β使A=r(ααT)=r(A)=      r(A)=r(A)=      r(A)=r(A)<      r(A)=

    特征值与特征向量

    ∑ i = 1 n λ i = ∏ i = 1 n λ i = \begin{aligned} & \sum_{i=1}^n \lambda_i =\\ \\ & \prod_{i=1}^n \lambda_i = \\\\ \end{aligned} i=1nλi=i=1nλi=

    矩 阵 A k A A k f ( A ) A − 1 A ∗ A − 1 + f ( A ) 特 征 值 对 应 的 特 征 向 量 \def\arraystretch{2} \begin{array}{c:c:c:c:c:c:c:c} 矩阵 & A & kA & A^k & f(A) & A^{-1} & A^* & A^{-1} + f(A) \\ \hline 特征值 & & & & & & & \\ \hline 对应的特征向量 & & &&&&& \end{array} AkAAkf(A)A1AA1+f(A)

    相似矩阵

    A ∼ B    ⟺       ⟺    ? ( 判 断 两 矩 阵 是 否 相 似 )    ⟹       ⟹       ⟹       ⟹       ⟹       ⟹       ⟹       ⟹       ⟹       ⟹       ⟹    \begin{aligned} & A \sim B \\ \\ \iff & \\\\ \iff & ?(判断两矩阵是否相似)\\\\\\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & \\ \\ \end{aligned} AB

    相似对角化

    A ∼ Λ    ⟺       ⟺    A = A T       ( A 是 实 对 称 矩 阵 )    ⟹       ⟹       ⟹    \begin{aligned} & A \sim \Lambda \\ \\ \iff & \\\\ \iff & \\ \\ \\ \\ & A = A^T ~~~~~(A是实对称矩阵) \\ \\ \implies & \\ \\ \implies & \\\\ \implies & \\ \\ \end{aligned} AΛA=AT     A

    正定二次型

    f = x T A x 正 定    ⟺       ⟺       ⟺       ⟺       ⟺       ⟺       ⟹       ⟹       ⟹    ? 正 定 ( A O O B ) 正 定    ⟺    \begin{aligned} & f=x^TAx正定 \\\\ \iff & \\ \\ \iff &\\ \\ \iff & \\ \\ \iff & \\ \\ \iff & \\ \\ \iff & \\ \\ \implies & \\ \\ \implies & \\ \\ \implies & ?正定 \\ \\ \\ & \begin{pmatrix} A & O \\ O & B \end{pmatrix} 正定 \iff \end{aligned} f=xTAx(AOOB)

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