LA-3-向量与线性方程组
2021-08-14 14:53:30 0 举报
AI智能生成
线性代数 第三章 向量 第四章 线性方程组 知识点梳理
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大纲/内容
线性方程组
齐次方程组Ax=0
基础解系
若<span class="equation-text" data-index="0" data-equation="\eta_1,\eta_2,...\eta_t" contenteditable="false"><span></span><span></span></span>是方程组的解,则Ax=0的通解为:<span class="equation-text" data-index="1" data-equation="\sum\limits_{i=1}^tk_i\eta_i" contenteditable="false"><span></span><span></span></span><br>
<span class="equation-text" data-index="0" data-equation="\eta_1,\eta_2,...\eta_t" contenteditable="false"><span></span><span></span></span>也就是基础解系,且必须满足:<span class="equation-text" data-index="1" data-equation="\left\{\begin{aligned}\eta_1,\eta_2,...\eta_t线性无关\\Ax=0任意解都可由\eta_1,\eta_2,...\eta_t线性表出\end{aligned}\right." contenteditable="false"><span></span><span></span></span>
方程组基础解系有n-r(A)个解<br>
有解判定<br>
有非零解
<font color="#F44336">n>r(A)</font>
<span class="equation-text" data-index="0" data-equation="\Leftrightarrow" contenteditable="false"><span></span><span></span></span>A的列向量组线性相关
无关无解有关有解
扁方阵
(方阵时)|A|=0
只有0解<br>
<font color="#F44336">n=r(A)</font>
<span class="equation-text" data-index="0" data-equation="\Leftrightarrow" contenteditable="false"><span></span><span></span></span>A的列向量组线性无关
(方阵时)|A|<span class="equation-text" data-index="0" data-equation="\neq" contenteditable="false"><span></span><span></span></span>0
n>r(A)
考察<br>
基础解系
求基础解系<br>
做初等行变换,整理成行最简矩阵
自由变量逐个令1(其余0),得基础解系<br>
自由变量分别令t、u、v...,得基础解系
有不止1种不等效的形式结果不同。<br>但此类“准行最简”亦可求解<br>
去掉第几列形成单位矩阵,去掉的就是自由变量
由基础解系求方程组<br>
由解求未知数个数,结合解的个数求A的秩<br>
例:求一个齐次线性方程组,使其基础解系为<br><span class="equation-text" data-index="0" data-equation="\eta_1=(4,3,1,2)^T,\eta_2=(0,1,3,-2)^T" contenteditable="false"><span></span><span></span></span><br>
if: r(A)<n方程有n-r个线性无关的解
例:若<span class="equation-text" data-index="0" data-equation="\alpha_1=(1,0,2)^T,\alpha_2=(0,1,-1)^T" contenteditable="false"><span></span><span></span></span>都<br>是Ax=0的解,则A可以是?<br>
Note:由题至少有两个线性无关的解,即n-r(A)≥2→r(A)≤1,A又不能是0,所以r(A)=1
对于<span class="equation-text" data-index="0" data-equation="A_{5\times4}" contenteditable="false"><span></span><span></span></span>,若<span class="equation-text" data-index="1" data-equation="\eta_1,\eta_2" contenteditable="false"><span></span><span></span></span>是Ax=0的基础解系,则r(<span class="equation-text" data-index="2" data-equation="A^T" contenteditable="false"><span></span><span></span></span>)?<br>
转置矩阵的定理:r(<span class="equation-text" data-index="0" data-equation="A^T" contenteditable="false"><span></span><span></span></span>)=r(A)<br>
对于<span class="equation-text" data-index="0" data-equation="A_{4\times5},\alpha_1,\alpha_2,\alpha_3" contenteditable="false"><span></span><span></span></span>是<span class="equation-text" data-index="1" data-equation="A^Tx=0" contenteditable="false"><span></span><span></span></span>的基础解系,求r(A)
抽象系数矩阵方程通解
活用AA*=A*A=|A|E直接得到一系列解
注意|A|=0
n-r(A)确定线性无关解的个数<br>
给不满秩系数矩阵求基础解系
非齐次方程组Ax=b
有解判定
有解:r(A)=r(<span class="equation-text" data-index="0" data-equation="\bar{A}" contenteditable="false"><span></span><span></span></span>)
唯一解:r(A)=r(<span class="equation-text" data-index="0" data-equation="\bar{A}" contenteditable="false"><span></span><span></span></span>)=n
无穷多解:r(A)=r(<span class="equation-text" data-index="0" data-equation="\bar{A}" contenteditable="false"><span></span><span></span></span>)<n
无解:r(A)<span class="equation-text" data-index="0" data-equation="\neq" contenteditable="false"><span></span><span></span></span>r(<span class="equation-text" data-index="1" data-equation="\bar{A}" contenteditable="false"><span></span><span></span></span>)
实际求解过程中,出现A全零且<span class="equation-text" data-index="0" data-equation="\bar A" contenteditable="false"><span></span><span></span></span>不全零行秩必不等,不一定在最下
可能全0是取特值才会出现的情况,注意讨论。
克拉默法则
有唯一解
det(A)<span class="equation-text" data-index="0" data-equation="\neq" contenteditable="false"><span></span><span></span></span>0<br>
<span class="equation-text" data-index="0" data-equation="X=A^{-1}b" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="x^i=\frac{|D_i|}{|D|}" contenteditable="false"><span></span><span></span></span>
解的结构
设<span class="equation-text" data-index="0" data-equation="\alpha" contenteditable="false"><span></span><span></span></span>是Ax=b的解,<span class="equation-text" data-index="1" data-equation="\eta_1,\eta_2,...\eta_t" contenteditable="false"><span></span><span></span></span>是方程组Ax=0的基础解系,则Ax=b的通解为:<span class="equation-text" data-index="2" data-equation="\alpha+\sum\limits_{i=1}^tk_i\eta_i" contenteditable="false"><span></span><span></span></span>
其中<span class="equation-text" data-index="0" data-equation="\alpha" contenteditable="false"><span></span><span></span></span>可以令自由变量全为0,直接通过原方程组解得
拓展(3(6))<br>
<span class="equation-text" data-index="0" data-equation="若\varphi_i为Ax=0的解" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="则\sum\limits_{i}k_i\varphi_i也是Ax=0的解" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="\varphi_i为Ax=0的基础解系" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="则\sum\limits_{i}k_i\varphi_i是Ax=0的通解" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="若\varphi_1,\varphi_0分别为Ax=0,Ax=b的特解" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="则\varphi_1+\varphi_0也是Ax=b的解" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="若\varphi_1为Ax=0通解,则\varphi_1+\varphi_0是Ax=b的通解" contenteditable="false"><span></span><span></span></span>
<br><span class="equation-text" data-index="0" data-equation="若\varphi_1,\varphi_2为Ax=b的特解" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="若\sum k_i=0\Leftrightarrow则\sum k_i\varphi_i是Ax=0的解" contenteditable="false"><span></span><span></span></span><br>
特例:<span class="equation-text" data-index="0" data-equation="\varphi_1{\color{red}-}\varphi_2是Ax=0的解" contenteditable="false"><span></span><span></span></span>
<span class="equation-text" data-index="0" data-equation="若\sum k_i=1\Leftrightarrow则\sum k_i\varphi_i是Ax=b的解" contenteditable="false"><span></span><span></span></span>
考察<br>
求解AX=b<br>
讨论r(A)和r(<span class="equation-text" data-index="0" data-equation="\bar A" contenteditable="false"><span></span><span></span></span>)<font color="#F44336">所有可能</font>的情况<br>
例
求解方程<span class="equation-text" data-index="0" data-equation="\begin{bmatrix}\lambda&1&1\\1&\lambda&1\\1&1&\lambda\end{bmatrix}\begin{bmatrix}x_1\\x_2\\x_3\end{bmatrix}=\begin{bmatrix}\lambda-3\\-2\\-2\end{bmatrix}" contenteditable="false"><span></span><span></span></span>
注意讨论无解
含多参方程组A(a,b...)X=b<br>不同解的情况下参数的取值<br>
按唯一解、无穷解、<br>无解的顺序讨论<br>
例
讨论方程<span class="equation-text" data-index="0" data-equation="\left\{\begin{aligned}&x_1+x_2+x_3&=&2&\\&x_1+2x_2+ax_3&=&-1&\\&2x_1+3x_2&=&b&\end{aligned}\right." contenteditable="false"><span></span><span></span></span>解的情况<br>给出对应的a、b取值,并求出解的表示。<br>
公共解、同解
同解方程组<br>
方程组具有相同的解的集合
方程组解集之联系
考察
抽象方程组解集之联系
设三阶矩阵<span class="equation-text" data-index="0" data-equation="A=[\alpha_1,\alpha_2,\alpha_3],B=[\beta_1,\beta_2,\beta_3]" contenteditable="false"><span></span><span></span></span>,其中<br><span class="equation-text" data-index="1" data-equation="\alpha" contenteditable="false"><span></span><span></span></span>可由<span class="equation-text" data-index="2" data-equation="\beta" contenteditable="false"><span></span><span></span></span>线性表出。则()<br>A. AX=0的解均为BX=0的解<br>B. A<span class="equation-text" data-index="3" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0的解均为B<span class="equation-text" data-index="4" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0的解<br>C. BX=0的解均为AX=0的解<br>D. B<span class="equation-text" data-index="5" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0的解均为A<span class="equation-text" data-index="6" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0的解
显而易见,可得<span class="equation-text" data-index="0" data-equation="[\alpha_1,\alpha_2,\alpha_3]=[\beta_1,\beta_2,\beta_3]\begin{bmatrix}c_{11}&c_{12}&c_{13}\\c_{21}&c_{22}&c_{23}\\c_{31}&c_{32}&c_{33}\end{bmatrix}" contenteditable="false"><span></span><span></span></span><br>即:A=BC,A<span class="equation-text" data-index="1" data-equation="^T" contenteditable="false"><span></span><span></span></span>=C<span class="equation-text" data-index="2" data-equation="^T" contenteditable="false"><span></span><span></span></span>B<span class="equation-text" data-index="3" data-equation="^T" contenteditable="false"><span></span><span></span></span><br>
对于任意X满足AX=0↔BCX=0,但不能推出BX=0→A错误<br>
对于任意X满足BX=0,不能推出BCX=0,从而不能得到AX=0→C错误
对于任意X满足A<span class="equation-text" data-index="0" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0↔C<span class="equation-text" data-index="1" data-equation="^T" contenteditable="false"><span></span><span></span></span>B<span class="equation-text" data-index="2" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0,但不能推出B<span class="equation-text" data-index="3" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0→B错误<br>
对于任意X满足B<span class="equation-text" data-index="0" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0,必有A<span class="equation-text" data-index="1" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=C<span class="equation-text" data-index="2" data-equation="^T" contenteditable="false"><span></span><span></span></span>B<span class="equation-text" data-index="3" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=C<span class="equation-text" data-index="4" data-equation="^T" contenteditable="false"><span></span><span></span></span>0=0,从而A<span class="equation-text" data-index="5" data-equation="^T" contenteditable="false"><span></span><span></span></span>X=0→D正确
考察方式
向量
基本概念
n个数构成的有序数组称为n维向量
行向量<span class="equation-text" data-index="0" data-equation="\boldsymbol a" contenteditable="false"><span></span><span></span></span>,列向量<span class="equation-text" data-index="1" data-equation="\boldsymbol a^T" contenteditable="false"><span></span><span></span></span>,0向量<br>
向量相等
加减法,数乘,点乘,叉乘<br>
解方程组
写出增广矩阵
初等行变换,化成阶梯型,再化成行最简矩阵
得到的矩阵对应的方程组称为同解方程组<br>
解的结构
线性表出
有m个向量<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_i" contenteditable="false"><span></span><span></span></span>,称<span class="equation-text" data-index="1" data-equation="\sum\limits_i^mk_i\boldsymbol\alpha_i" contenteditable="false"><span></span><span></span></span>为向量<span class="equation-text" data-index="2" data-equation="\boldsymbol\alpha_1,...\boldsymbol\alpha_m" contenteditable="false"><span></span><span></span></span>的一个线性组合<br>
可表示为<span class="equation-text" data-index="0" data-equation="[\vec\alpha_1,\vec\alpha_2,...\vec\alpha_m]\left[\begin{aligned}k_1\\k_2\\\vdots\\k_m\end{aligned}\right]=\vec\beta" contenteditable="false"><span></span><span></span></span><br>这是一个方程组的形式。<span class="equation-text" data-index="1" data-equation="\vec k" contenteditable="false"><span></span><span></span></span>是解向量<br>
若向量<span class="equation-text" data-index="0" data-equation="\boldsymbol\beta" contenteditable="false"><span></span><span></span></span>能表示为<span class="equation-text" data-index="1" data-equation="\boldsymbol\alpha_1,...\boldsymbol\alpha_m" contenteditable="false"><span></span><span></span></span>的线性组合<br>则称向量<span class="equation-text" data-index="2" data-equation="\boldsymbol\beta" contenteditable="false"><span></span><span></span></span>可由<span class="equation-text" data-index="3" data-equation="\boldsymbol\alpha_1,...\boldsymbol\alpha_m" contenteditable="false"><span></span><span></span></span>线性表出<br>
能,唯一
能,无数种
不能
若<span class="equation-text" data-index="0" data-equation="\vec\alpha" contenteditable="false"><span></span><span></span></span>可由向量组<span class="equation-text" data-index="1" data-equation="\vec\beta_i" contenteditable="false"><span></span><span></span></span>线性表出,<br>且<span class="equation-text" data-index="2" data-equation="\vec\beta_i" contenteditable="false"><span></span><span></span></span>可由向量组<span class="equation-text" data-index="3" data-equation="\vec\gamma_j" contenteditable="false"><span></span><span></span></span>线性表出,<br>则<span class="equation-text" data-index="4" data-equation="\vec\alpha" contenteditable="false"><span></span><span></span></span>可由向量组<span class="equation-text" data-index="5" data-equation="\vec\gamma_j" contenteditable="false"><span></span><span></span></span>线性表出
例:设<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_1=(1,2,3)^\bold T,\boldsymbol\alpha_2=(1,3,4)^\bold T,\boldsymbol\alpha_3=(2,-1,1)^\bold T" contenteditable="false"><span></span><span></span></span><br><span class="equation-text" data-index="1" data-equation="\boldsymbol\beta=(2,5,t)^\bold T" contenteditable="false"><span></span><span></span></span>问当t取何值时,<span class="equation-text" data-index="2" data-equation="\vec\beta" contenteditable="false"><span></span><span></span></span>不能由<span class="equation-text" data-index="3" data-equation="\vec\alpha_{1,2,3}" contenteditable="false"><span></span><span></span></span>线性表出?
例:设<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_1=(1+\lambda,1,1)^\bold T,\boldsymbol\alpha_2=(1,1+\lambda,1)^\bold T,\boldsymbol\alpha_3=(1,1,1+\lambda)^\bold T" contenteditable="false"><span></span><span></span></span><br><span class="equation-text" data-index="1" data-equation="\boldsymbol\beta=(0,\lambda,\lambda^2)^\bold T" contenteditable="false"><span></span><span></span></span>问当<span class="equation-text" data-index="2" data-equation="\lambda" contenteditable="false"><span></span><span></span></span>取何值时,<span class="equation-text" data-index="3" data-equation="\vec\beta" contenteditable="false"><span></span><span></span></span>不能由<span class="equation-text" data-index="4" data-equation="\vec\alpha_{1,2,3}" contenteditable="false"><span></span><span></span></span>线性表出?若要使<span class="equation-text" data-index="5" data-equation="\vec\beta" contenteditable="false"><span></span><span></span></span>可以<br>由<span class="equation-text" data-index="6" data-equation="\vec\alpha_{1,2,3}" contenteditable="false"><span></span><span></span></span>线性表出,给出<span class="equation-text" data-index="7" data-equation="\vec\beta" contenteditable="false"><span></span><span></span></span>的表达式<br>
例:设<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_1=(1,2,1)^\bold T,\boldsymbol\alpha_2=(2,3,a)^\bold T,\boldsymbol\beta_1=(1,3,0)^\bold T," contenteditable="false"><span></span><span></span></span><br><span class="equation-text" data-index="1" data-equation="\boldsymbol\beta_2=(1,a+2,-2)^\bold T" contenteditable="false"><span></span><span></span></span>问当<span class="equation-text" data-index="2" data-equation="a" contenteditable="false"><span></span><span></span></span>取何值时,<span class="equation-text" data-index="3" data-equation="\vec\beta_1" contenteditable="false"><span></span><span></span></span>不能由<span class="equation-text" data-index="4" data-equation="\vec\alpha_{1,2}" contenteditable="false"><span></span><span></span></span>线性<br>表出且<span class="equation-text" data-index="5" data-equation="\vec\beta_2" contenteditable="false"><span></span><span></span></span>可以由<span class="equation-text" data-index="6" data-equation="\vec\alpha_{1,2}" contenteditable="false"><span></span><span></span></span>线性表出?<br>
不宜分开求解,可以合并矩阵进行分析<br>
线性相关性
有m个向量<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_i" contenteditable="false"><span></span><span></span></span>,若<span class="equation-text" data-index="1" data-equation="\exists" contenteditable="false"><span></span><span></span></span>不全为0的实数<span class="equation-text" data-index="2" data-equation="k_1,k_2...k_m" contenteditable="false"><span></span><span></span></span>使<span class="equation-text" data-index="3" data-equation="\sum\limits_i^mk_i\boldsymbol\alpha_i=0" contenteditable="false"><span></span><span></span></span>,<br>则称向量组<span class="equation-text" data-index="4" data-equation="\boldsymbol\alpha_i" contenteditable="false"><span></span><span></span></span>线性相关,否则称线性无关(存在相关,不存在无关)
不要求向量组不含0向量
含0向量的向量组必然线性相关
可表示为<span class="equation-text" data-index="0" data-equation="[\vec\alpha_1,\vec\alpha_2,...\vec\alpha_m]\left[\begin{aligned}k_1\\k_2\\...\\k_m\end{aligned}\right]=0" contenteditable="false"><span></span><span></span></span>
是否线性无关/相关:列方程组,求是否有<font color="#F44336">非零解</font>
系数矩阵秩<font color="#F44336"><</font>m↔有非零解→线性相关
n个n维向量线性相关↔det(<span class="equation-text" data-index="0" data-equation="[\vec\alpha_1,\vec\alpha_2,...\vec\alpha_m]" contenteditable="false"><span></span><span></span></span>)=0
n+1个n维向量必然线性相关<br>
无→线性无关
例
设<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_1=(1,3,2)^\bold T,\boldsymbol\alpha_2=(-1,1,2)^\bold T,\boldsymbol\alpha_3=(0,a,3)^\bold T" contenteditable="false"><span></span><span></span></span>线性相关,求a<br>
设<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_1=(3,4,2)^\bold T,\boldsymbol\alpha_2=(2,1,-7)^\bold T,\boldsymbol\alpha_3=(1,2,4)^\bold T" contenteditable="false"><span></span><span></span></span>是否线性相关?<br>
设<span class="equation-text" data-index="0" data-equation="\boldsymbol\alpha_1=(1,2)^\bold T,\boldsymbol\alpha_2=(3,4)^\bold T,\boldsymbol\alpha_3=(5,6)^\bold T" contenteditable="false"><span></span><span></span></span>是否线性相关?<br>
向量组
秩<br>Rank<br>
所谓Rank即为行
<font color="#F44336">向量组的秩就是其最大无关组所含的向量个数</font>
正交矩阵
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