向量内积的推导

 基本式

\mathbf{x}\cdot\mathbf{y}=x_1y_1+\cdots+x_ny_n=\displaystyle\sum_{i=1}^{n}x_iy_i

\mathbf{x}^T\mathbf{y}=\begin{bmatrix} x_1&\cdots&x_n \end{bmatrix}\begin{bmatrix}  y_1\\  \vdots\\  y_n  \end{bmatrix}=\displaystyle\sum_{i=1}^{n}x_iy_i

几何

\left\langle\mathbf{x},\mathbf{y}\right\rangle=\Vert\mathbf{x}\Vert\,\Vert\mathbf{y}\Vert\,\cos\theta

 

\begin{aligned} \cos(\alpha-\beta)&=\cos\alpha\,\cos\beta+\sin\alpha\,\sin\beta\\ &=\displaystyle\frac{x_1}{\Vert\mathbf{x}\Vert}\frac{y_1}{\Vert\mathbf{y}\Vert}+\frac{x_2}{\Vert\mathbf{x}\Vert}\frac{y_2}{\Vert\mathbf{y}\Vert}\\ &=\frac{x_1y_1+x_2y_2}{\Vert\mathbf{x}\Vert~\Vert\mathbf{y}\Vert}.\end{aligned}

 

\left\langle\mathbf{x},\mathbf{y}\right\rangle=x_1y_1+x_2y_2

 

  1. 對稱性:

    \begin{aligned} \left\langle\mathbf{x},\mathbf{y}\right\rangle&=x_1y_1+x_2y_2=y_1x_1+y_2x_2=\left\langle\mathbf{y},\mathbf{x}\right\rangle\end{aligned}

  2. 線性函數:設 \mathbf{x},\mathbf{y},\mathbf{z}\in\mathbb{R}^2。固定 \mathbf{x} 時,

     

    \begin{aligned} \left\langle\mathbf{x},\mathbf{y}+\mathbf{z}\right\rangle&=x_1(y_1+z_1)+x_2(y_2+z_2)\\ &=(x_1y_1+x_2y_2)+(x_1z_1+x_2z_2)\\ &=\left\langle\mathbf{x},\mathbf{y}\right\rangle+\left\langle\mathbf{x},\mathbf{z}\right\rangle,\end{aligned}

    而且

    \begin{aligned} \left\langle\mathbf{x},c\mathbf{y}\right\rangle&=x_1(cy_1)+x_2(cy_2)\\ &=c(x_1y_1+x_2y_2)\\ &=c\left\langle\mathbf{x},\mathbf{y}\right\rangle.\end{aligned}

    同樣道理,固定 \mathbf{y} 時,

    \begin{aligned} \left\langle\mathbf{x}+\mathbf{z},\mathbf{y}\right\rangle&=\left\langle\mathbf{x},\mathbf{y}\right\rangle+\left\langle\mathbf{z},\mathbf{y}\right\rangle\\ \left\langle c\mathbf{x},\mathbf{y}\right\rangle&=c\left\langle\mathbf{x},\mathbf{y}\right\rangle.\end{aligned}

 

posted @ 2016-08-06 08:11  kyo.stone  阅读(1743)  评论(0编辑  收藏  举报