$$a+b=c$$
$$ X \mathop{\rightarrow}^{ P} Y$$
$ X \mathop{\rightarrow}^{ P} Y$
$$E_1 \times E_2 \equiv E_2 \times E_1
E_1 \bowtie E_2 \equiv E_2 \bowtie E_1
E_1 \mathop{\bowtie}_{F} E_2 \equiv E_2 \mathop{\bowtie}_{F}E_1 $$
$$\cos 2\theta = \cos^2 \theta - \sin^2 \theta = 2 \cos^2 \theta$$
$$ \sum_{i=0}^n i^2 = \frac{(n^2+n)(2n+1)}{6} $$
$$ x = \dfrac{-b \pm \sqrt{b^2 - 4ac}}{2a} $$
$$
f(n) =
\begin{cases}
n/2, & \text{if $n$ is even} \
3n+1, & \text{if $n$ is odd}
\end{cases}
$$

$$ R \cup S={t|t \in R \wedge t \in S }$$
$$ \bowtie _{a+b}^{} 23333$$
$$ {\begin{matrix}R \bowtie S \ a \theta b \end{matrix}} $$
$$ \sum _{k=1}^{n} $$

$${\begin{matrix}R\ \bowtie \ S\a\ \theta \ b\end{matrix}} $$
$$ {\begin{matrix}R \bowtie S\a \theta b\end{matrix}} $$
$$\mathop{ R \bowtie S}_{a \theta b} = { \mathop{t_rt_s}^{ \frown} | t_r \in R \wedge t \in S \wedge t_r[A] \theta t_s[B]}$$
⋉ ▷ ⋊
$$
\usepackage{wasysym}
R \bowtie S
\Bowtie
$$

$$R \under{\bowtie}_{233} S$$

在Unicode中，左外连接符号 : ⟕ ⟕
在Unicode中，右外连接符号是 ⟖ ⟖
在Unicode中，全外连接符号是 ⟗ ⟗

$$R \times S ={r \cup s| r \in R, s \in S }$$
$${\displaystyle X\times Y=\left{\left(x,y\right)\mid x\in X\land y\in Y\right}}。$$
$$R \times S ={ \usepackage{t_r, t_s} t_r \frown t_s | t_r \in R, t_s \in S }$$
$$\overline{a+b+c+d} $$
$$ \usepackage{a,b}$$
$$ \frown{a,b}$$
$$ t_r^\frown t_s $$
设关系模式$R(A_1,A_2…A_n)$,它的一个关系为R。t $\in$ R 表示 t 是R的一个元组。$t[A_i]$则表示元组 t 中相应于属性$A_i$的一个分量。

$$\sigma_F(R)$$

$$
\begin{equation}
\begin{split}
\frac{\partial^2 f}{\partial{x^2}} &= \frac{\partial(\Delta_x f(i,j))}{\partial x} = \frac{\partial(f(i+1,j)-f(i,j))}{\partial x} \
&= \frac{\partial f(i+1,j)}{\partial x} - \frac{\partial f(i,j)}{\partial x} \
&= f(i+2,j) -2f(f+1,j) + f(i,j)
\end{split}
\nonumber
\end{equation}
$$

$$
\begin{equation}
\sum_{i=0}^n F_i \cdot \phi (H, p_i) - \sum_{i=1}^n a_i \cdot ( \tilde{x_i}, \tilde{y_i}) + b_i \cdot ( \tilde{x_i}^2 , \tilde{y_i}^2 )
\end{equation}
$$
$$
\begin{equation}
\beta^*(D) = \mathop{argmin} \limits_{\beta} \lambda {||\beta||}^2 + \sum_{i=1}^n max(0, 1 - y_i f_{\beta}(x_i))
\end{equation}
$$

$$
\begin{equation}
\sum_{i=0}^n F_i \cdot \phi (H, p_i) - \sum_{i=1}^n a_i \cdot ( \tilde{x_i}, \tilde{y_i}) + b_i \cdot ( \tilde{x_i}^2 , \tilde{y_i}^2 )
\nonumber
\end{equation}
$$
$$
\begin{equation}
\beta^*(D) = \mathop{argmin} \limits_{\beta} \lambda {||\beta||}^2 + \sum_{i=1}^n max(0, 1 - y_i f_{\beta}(x_i))
\end{equation}
$$

$$
\begin{equation}
\sum_{i=0}^n F_i \cdot \phi (H, p_i) - \sum_{i=1}^n a_i \cdot ( \tilde{x_i}, \tilde{y_i}) + b_i \cdot ( \tilde{x_i}^2 , \tilde{y_i}^2 ) \tag{1.2.3}
\end{equation}
$$

$$
\left(
\begin{array}{c}
s \
t
\end{array}
\right)
=
\left(
\begin{array}{cc}
cos(b) & -sin(b) \
sin(b) & cos(b)
\end{array}
\right)
\left(
\begin{array}{c}
x \
y
\end{array}
\right)
$$

$$
\left[
\begin{array}{cc|c}
1&2&3\
4&5&6
\end{array}
\right]
$$