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线性代数1.矩阵的基本概念&意义&特殊矩阵&基本运算

2024-12-27 14:30:15 发布   74 浏览  
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1.矩阵的基本概念&意义&特殊矩阵&基本运算

1.1 矩阵的定义:

矩阵是由(m times n)个数排成的数表。

如以下矩阵:

[X= begin{bmatrix} x_{11} & x_{12} & x_{13} & ... & x_{1n}\ x_{21} & x_{22} & x_{23} & ... & x_{2n}\ x_{31} & x_{32} & x_{33} & ... & x_{3n}\ &&......\ x_{m1} & x_{m2} & x_{m3} & ... & x_{mn}\ end{bmatrix} ]

其中:

(1) (X)为矩阵名称,亦可记为(X_{mn})

(2) (x_{ij}(i=1,2,3,...,m;j=1,2,3,...,n))为矩阵X中的元素,简称元

(3)(x_{ij})可称为X的(i,j)元;X矩阵亦可记为((x_{ij}))矩阵或((x_{ij})_{mn})矩阵

1.2矩阵的意义

若存在变量(x_i),变量(y_j),系数(a_{ij}),其中(i=1,2,3,...,m),(j=1,2,3,...,n)

则可用矩阵表示(x_i)(y_j)的线性变换:

[begin{cases} y_1=a_{11}x_1+a_{12}x_2+a_{13}x_3+...+a_{1n}x_n\ y_2=a_{21}x_1+a_{22}x_2+a_{23}x_3+...+a_{2n}x_n\ y_3=a_{31}x_1+a_{32}x_2+a_{33}x_3+...+a_{3n}x_n\ ......\ y_m=a_{m1}x_1+a_{m2}x_2+a_{m3}x_3+...+a_{mn}x_n end{cases} ]

1.3特殊矩阵

1.3.1 单位矩阵

若存在变量(x_i),变量(y_j),其中(i=1,2,3,...,m),(j=1,2,3,...,n),且(x_i)(y_j)的线性变换满足:

[begin{cases} y_1=x_1\ y_2=x_2\ y_3=x_3\ ......\ y_m=x_n\ end{cases} ]

则称(x_i)(y_j)的变换为(恒等变换),对应的矩阵称为(单位矩阵),用字母(E)或字母(I)表示:

[E= begin{bmatrix} 1 &0&...&0 &0 &0 \ 0 &1 & 0 &...&0 &0\ 0 & 0 & 1 &0 &... &0\ & & &......\ 0 & 0 & 0 &... &1 &0\ 0 & 0 & 0 &... &0 &1 end{bmatrix}\ ]

1.3.2 对角矩阵

若存在变量(x_i),变量(y_j),其中(i=j=1,2,3,...,n),且(x_i)(y_j)的线性变换满足:

[begin{cases} y_1=lambda_1 x_1\ y_2=lambda_2 x_2\ y_3=lambda_3 x_3\ ......\ y_n=lambda_n x_n\ end{cases} ]

则对应的矩阵称为(对角矩阵),可用任意大写字母表示:

[A= begin{bmatrix} lambda_1 &0&...&0 &0 &0 \ 0 &lambda_2 & 0 &...&0 &0\ 0 & 0 & lambda_3 &0 &... &0\ & & &......\ 0 & 0 & 0 &... &0 &lambda_n end{bmatrix}\ ]

1.4矩阵的基本运算

设存在以下矩阵:

[X= begin{bmatrix} x_{11} & x_{12} & x_{13} & ... & x_{1n}\ x_{21} & x_{22} & x_{23} & ... & x_{2n}\ x_{31} & x_{32} & x_{33} & ... & x_{3n}\ &&......\ x_{m1} & x_{m2} & x_{m3} & ... & x_{mn}\ end{bmatrix} ]

[Y= begin{bmatrix} y_{11} & y_{12} & y_{13} & ... & y_{1n}\ y_{21} & y_{22} & y_{23} & ... & y_{2n}\ y_{31} & y_{32} & y_{33} & ... & y_{3n}\ &&......\ y_{m1} & y_{m2} & y_{m3} & ... & y_{mn}\ end{bmatrix} ]

1.4.1 矩阵的加法运算

  • 根据已知的X、Y矩阵,可得:

[X+Y= begin{bmatrix} x_{11}+y_{11} & x_{12}+y_{12} & x_{13}+y_{13} & ... & x_{1n}+y_{1n}\ x_{21}+y_{21} & x_{22}+y_{22} & x_{23}+y_{23} & ... & x_{2n}+y_{2n}\ x_{31}+y_{31} & x_{32}+y_{32} & x_{33}+y_{33} & ... & x_{3n}+y_{3n}\ &&......\ x_{m1}+y_{m1} & x_{m2}+y_{m2} & x_{m3}+y_{m3} & ... & x_{mn}+y_{mn}\ end{bmatrix} ]

  • 矩阵的加法运算律:

[tag{1}X+Y=Y+X ]

[tag{2}(X+Y)+Z=X+(Y+Z) ]

1.4.2 矩阵的乘法运算

  • 根据已知的X矩阵,数$lambda $与矩阵X相乘可得:

[lambda X= begin{bmatrix} lambda x_{11} & lambda x_{12} & lambda x_{13} & ... & lambda x_{1n}\ lambda x_{21} & lambda x_{22} & lambda x_{23} & ... & lambda x_{2n}\ lambda x_{31} & lambda x_{32} & lambda x_{33} & ... & lambda x_{3n}\ &&......\ lambda x_{m1} & lambda x_{m2} & lambda x_{m3} & ... & lambda x_{mn}\ end{bmatrix} ]

  • 根据已知的X矩阵、Y矩阵相乘可得:

[X times Y= begin{bmatrix} z_{11} &z_{12} &z_{13} ... &z_{1n}\ z_{21} &z_{22} &z_{23} ... &z_{2n} \ ...\ z_{m1} &z_{n2} &z_{n3} ... &z_{mn}\ end{bmatrix} ]

[其中:\ z_{11}=x_{11}y_{11}+x_{12}y_{21}+x_{13}y_{31}+...+x_{1n}y_{m1}\ z_{12}=x_{11}y_{12}+x_{12}y_{22}+x_{13}y_{32}+...+x_{1n}y_{m2}\ ...\ z_{21}=x_{21}y_{11}+x_{22}y_{21}+x_{23}y_{31}+...+x_{2n}y_{m1}\ ...\ z_{mn}=x_{m1}y_{1n}+x_{m2}y_{2n}+x_{m3}y_{3n}+...+x_{mn}y_{mn} ]

  • 矩阵的乘法运算律

[tag{1} (lambda mu) A=lambda (mu A) ]

[ tag{2}(lambda+mu) A=lambda A + mu A ]

[tag{3}lambda (A+B)=lambda A+lambda B ;lambda (AB)=(lambda A)B=A(lambda B) ]

[tag{4}(AB)C=A(BC) ]

[]

[tag{5}A(B+C)=AB+AC;(B+C)A=BA+CA ]

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