第一章,06-行列式的降阶计算-余子式和代数余子式

简介

这是《玩转线性代数》的学习笔记。
示例请查看原文

余子式

在n阶行列式中,把元素a i j a_{ij}aij所在的第i行与第j列划去后留下的n − 1 n-1n1阶行列式叫做元素a i j a_{ij}aij的余子式,记作M i j M_{ij}Mij

代数余子式

在n阶行列式中,记A i j = ( − 1 ) i + j M i j A_{ij}=(-1)^{i+j}M_{ij}Aij=(1)i+jMij为元素的代数余子式。

代数余子式相关定理

一个n阶行列式,如果其中第i行只有一个a i j a_{ij}aij非零,则行列式D = a i j A i j D=a_{ij}A_{ij}D=aijAij
由于转置不改变行列式的值,行和列其实是等价的,所以这定理对列也成立。

行列式按行(列)展开法则

n阶行列式D DD等于它的任一行(列)的各元素与其对应的代数余子式的乘积之和,即
D = a i 1 A i 1 + a i 2 A i 2 + ⋯ + a i n A i n = ∑ k = 1 n a i k A i k ( i = 1 , 2 , ⋯ , n ) , D=a_{i1}A_{i1}+a_{i2}A_{i2}+\cdots+a_{in}A_{in}=\sum_{k=1}^n a_{ik}A_{ik}\quad(i=1,2,\cdots,n),D=ai1Ai1+ai2Ai2++ainAin=k=1naikAik(i=1,2,,n)

D = a 1 j A 1 j + a 2 j A 2 j + ⋯ + a n j A n j = ∑ k = 1 n a k j A k j ( j = 1 , 2 , ⋯ , n ) 。 D=a_{1j}A_{1j}+a_{2j}A_{2j}+\cdots+a_{nj}A_{nj}=\sum_{k=1}^n a_{kj}A_{kj}\quad(j=1,2,\cdots,n)。D=a1jA1j+a2jA2j++anjAnj=k=1nakjAkj(j=1,2,,n)

证明

任选D DD的第i行,把该行元素都写作n个数之和:
D = ∣ a 11 a 12 ⋯ a 1 n ⋮ ⋮ ⋮ a i 1 a i 2 ⋯ a i n ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ = ∣ a 11 a 12 ⋯ a 1 n ⋮ ⋮ ⋮ a i 1 + 0 + ⋯ + 0 0 + a i 2 + ⋯ + 0 ⋯ 0 + ⋯ + 0 + a i n ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ = ∣ a 11 a 12 ⋯ a 1 n ⋮ ⋮ ⋮ a i 1 0 ⋯ 0 ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ + ∣ a 11 a 12 ⋯ a 1 n ⋮ ⋮ ⋮ 0 a i 2 ⋯ 0 ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ + ⋯ + ∣ a 11 a 12 ⋯ a 1 n ⋮ ⋮ ⋮ 0 0 ⋯ a i n ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ D=\begin{vmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ \vdots & \vdots & & \vdots \\ a_{i1} & a_{i2} & \cdots &a_{in} \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix} =\begin{vmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ \vdots & \vdots & & \vdots \\ a_{i1}+0+\cdots+0 & 0+a_{i2}+\cdots+0 & \cdots &0+\cdots+0+a_{in} \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix}\\ =\begin{vmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ \vdots & \vdots & & \vdots \\ a_{i1} & 0& \cdots &0 \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix} +\begin{vmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ \vdots & \vdots & & \vdots \\ 0& a_{i2} & \cdots &0 \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix} +\cdots+ \begin{vmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ \vdots & \vdots & & \vdots \\ 0 & 0 & \cdots &a_{in} \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix}D=a11ai1an1a12ai2an2a1nainann=a11ai1+0++0an1a120+ai2++0an2a1n0++0+ainann=a11ai1an1a120an2a1n0ann+a110an1a12ai2an2a1n0ann++a110an1a120an2a1nainann

D = a i 1 A i 1 + a i 2 A i 2 + ⋯ + a i n A i n = ∑ k = 1 n a i k A i k ( i = 1 , 2 , ⋯ , n ) D=a_{i1}A_{i1}+a_{i2}A_{i2}+\cdots+a_{in}A_{in}=\sum_{k=1}^n a_{ik}A_{ik}\quad(i=1,2,\cdots,n)D=ai1Ai1+ai2Ai2++ainAin=k=1naikAik(i=1,2,,n)

展开定理的推论

行列式任一行(列)的元素与另一行(列)的对应元素的代数余子式乘积之和等于0。即:
a i 1 A j 1 + a i 2 A j 2 + ⋯ + a i n A j n = 0 , i ≠ j a 1 i A 1 j + a 2 i A 2 j + ⋯ + a n i A n j = 0 , i ≠ j a_{i1}A_{j1}+a_{i2}A_{j2}+\cdots+a_{in}A_{jn}=0,i \neq j a_{1i}A_{1j}+a_{2i}A_{2j}+\cdots+a_{ni}A_{nj}=0,i \neq jai1Aj1+ai2Aj2++ainAjn=0,i=ja1iA1j+a2iA2j++aniAnj=0,i=j

例:
∣ a 11 a 12 ⋯ a 1 n a 21 a 22 ⋯ a 2 n ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ = a 11 A 11 + a 12 A 12 + ⋯ + a 1 n A 1 n \begin{vmatrix} a_{11} & a_{12} & \cdots & a_{1n} \\ a_{21} & a_{22} & \cdots &a_{2n} \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix} =a_{11}A_{11}+a_{12}A_{12}+\cdots+a_{1n}A_{1n}a11a21an1a12a22an2a1na2nann=a11A11+a12A12++a1nA1n
将等式两边的a 11 a_{11}a11换成a 21 a_{21}a21a 12 a_{12}a12换成a 22 a_{22}a22a 1 n a_{1n}a1n换成a 2 n a_{2n}a2n,即用第二行元素替换第一行,对应行列式为:
a 21 A 11 + a 22 A 12 + ⋯ + a 2 n A 1 n = ∣ a 21 a 22 ⋯ a 2 n a 21 a 22 ⋯ a 2 n ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ = 0 。 a_{21}A_{11}+a_{22}A_{12}+\cdots+a_{2n}A_{1n} =\begin{vmatrix} a_{21} & a_{22} & \cdots & a_{2n} \\ a_{21} & a_{22} & \cdots &a_{2n} \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix} =0。a21A11+a22A12++a2nA1n=a21a21an1a22a22an2a2na2nann=0

另:
将等式两边的a 11 a_{11}a11换成b 1 b_{1}b1a 12 a_{12}a12换成b 2 b_{2}b2a 1 n a_{1n}a1n换成b n b_{n}bn,即用第二行元素替换第一行,对应行列式为:
b 1 A 11 + b 2 A 12 + ⋯ + b n A 1 n = ∣ b 1 b 2 ⋯ b n a 21 a 22 ⋯ a 2 n ⋮ ⋮ ⋮ a n 1 a n 2 ⋯ a n n ∣ b_{1}A_{11}+b_{2}A_{12}+\cdots+b_{n}A_{1n} =\begin{vmatrix} b_{1} & b_{2} & \cdots & b_{n} \\ a_{21} & a_{22} & \cdots &a_{2n} \\ \vdots & \vdots & & \vdots \\ a_{n1} & a_{n2} & \cdots & a_{nn} \end{vmatrix}b1A11+b2A12++bnA1n=b1a21an1b2a22an2bna2nann
即要计算某行(列)元素代数余子式的组合b 1 A 11 + b 2 A 12 + ⋯ + b n A 1 n b_{1}A_{11}+b_{2}A_{12}+\cdots+b_{n}A_{1n}b1A11+b2A12++bnA1n时,只要将该行(列)替换为系数即可。


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