typora中输入数学公式

LaTeX编辑数学公式基本语法元素

LaTeX中数学模式有两种形式:inline和display。前者是指正文插入行间数学公式,后者独立排列,可以有或没有编号。

  • 行间公式(inline):用$...$将公式括起来
  • 块间公式(display):用$$...$$将公式括起来,默认显示在行中间
  • 各类希腊字母表
希腊字母英文希腊字母英文希腊字母英文希腊字母英文
α \alphaα\alphaθ \thetaθ\thetao oooτ \tauτ\tau
β \betaβ\betaϑ \varthetaϑ\varthetaπ \piπ\piυ \upsilonυ\upsilon
γ \gammaγ\gammaι \iotaι\iotaϖ \varpiϖ\varpiϕ \phiϕ\phi
δ \deltaδ\deltaκ \kappaκ\kappaρ \rhoρ\rhoφ \varphiφ\varphi
ϵ \epsilonϵ\epsilonλ \lambdaλ\lambdaϱ \varrhoϱ\varrhoχ \chiχ\chi
ε \varepsilonε\varepsilonμ \muμ\muσ \sigmaσ\sigmaψ \psiψ\psi
ζ \zetaζ\zetaν \nuν\nuς \varsigmaς\varsigmaω \omegaω\omega
η \etaη\etaξ \xiξ\xi
Γ \GammaΓ\GammaΛ \LambdaΛ\LambdaΣ \SigmaΣ\SigmaΨ \PsiΨ\Psi
Δ \DeltaΔ\DeltaΞ \XiΞ\XiΥ \UpsilonΥ\UpsilonΩ \OmegaΩ\Omega
Θ \ThetaΘ\ThetaΠ \PiΠ\PiΦ \PhiΦ\Phi

上下标、根号、省略号

  • 下标:$x_i$ --> x i x_ixi

  • 上标:$x^2$ --> x 2 x^2x2

    注意:上下标如果多余一个字母或者符号,需要用一对{}括起来:

    • $x _ {i1}$ --> x i 1 x _ {i1}xi1
    • $x^{\alpha t}$ --> x α t x ^ {\alpha t}xαt
  • 根号:\sqrt , eg: $\sqrt[n]{5}$ --> 5 n \sqrt[n]{5}n5

  • 省略号:\dots \cdots 分别表示 … \dots, ⋯ \cdots

运算符

  • 基本运算符:+ - * /等可以直接输入,其他特殊的有:

    \pm\times\div\cdot\cap\cup\geq\leq\neq\approx\equix
    ± \pm±× \times×÷ \div÷⋅ \cdot∩ \cap∪ \cup≥ \geq≤ \leq≠ \neq=≈ \approx≡ \equiv
  • 求和:$\sum_1^n$ : ∑ 1 n \sum_1^n1n

  • 累乘:$\prod_{n=1}^{99}x_n$:∏ n = 1 99 x n \prod_{n=1}^{99}x_nn=199xn

  • 积分: $\int_1^n$ : ∫ 1 n \int_1^n1n

  • 极限:

    • \lim\limits _ {x \to \infty} : lim ⁡ x → 0 \lim\limits _ {x \to 0}x0lim

分数

分数的表示:\frac{}{},如$\frac{3}{8}$ ==> 3 8 \frac{3}{8}83

矩阵和行列式

矩阵

==$$\begin{matrix}…\end{matrix}$$,使用&分隔同行元素,\\表示换行:

示例:

$$
\begin{matrix}
1&x&x^2\\
1&y&y^2\\
1&z&z^2\\
\end{matrix}
$$

结果:
1 x x 2 1 y y 2 1 z z 2 \begin{matrix} 1&x&x^2\\ 1&y&y^2\\ 1&z&z^2\\ \end{matrix}111xyzx2y2z2

行列式

示例:

$$
X=\left|
	\begin{matrix}
	x_{11} & x_{12} & \cdots & x_{1d}\\
	x_{21} & x_{22} & \cdots & x_{2d}\\
	\vdots & \vdots & \ddots & \vdots\\
	x_{m1} & x_{m2} & \cdots & x_{md} \\
    \end{matrix}
    \right|
$$

结果:
X = ∣ x 11 x 12 ⋯ x 1 d x 21 x 22 ⋯ x 2 d ⋮ ⋮ ⋱ ⋮ x m 1 x m 2 ⋯ x m d ∣ X=\left| \begin{matrix} x_{11} & x_{12} & \cdots & x_{1d}\\ x_{21} & x_{22} & \cdots & x_{2d}\\ \vdots & \vdots & \ddots & \vdots\\ x_{m1} & x_{m2} & \cdots & x_{md} \\ \end{matrix} \right|X=x11x21xm1x12x22xm2x1dx2dxmd

箭头

符号表达式符号表达式
← \leftarrow\lefrarrow⟵ \longleftarrow\longleftarrow
→ \rightarrow\rightarrow⟶ \longrightarrow\longrightarrow
↔ \leftrightarrow\leftrightarrow⟷ \longleftrightarrow\longleftrightarrow
⇐ \Leftarrow\Leftarrow⟸ \Longleftarrow\Longleftarrow
⇒ \Rightarrow\Rightarrow⟹ \Longrightarrow\Longrightarrow
⇔ \Leftrightarrow\Leftrightarrow⟺ \Longleftrightarrow\Longleftrightarrow

方程式

$$
\begin{equation}
E=mc^2
\end{equation}
$$

KaTeX parse error: No such environment: equation at position 8: \begin{̲e̲q̲u̲a̲t̲i̲o̲n̲}̲ E=mc^2 \end{eq…

分隔符

各种括号用 () [] { } \langle\rangle 等命令表示,注意花括号通常用来输入命令和环境的参数,所以在数学公式中它们前面要加 \。可以在上述分隔符前面加 \big \Big \bigg \Bigg 等命令来调整大小。

$$
\max \limits_{a<x<b} \Bigg\{f(x)\Bigg\}
$$

max ⁡ a < x < b { f ( x ) } \max \limits_{a<x<b} \Bigg\{f(x)\Bigg\}a<x<bmax{f(x)}

分段函数

$$
f(n) = 
\begin{cases}
n/2, & \text {if $n$ is even}\\
3n+1, & \text {if $n$ is odd}
\end{cases}
$$

f ( n ) = { n / 2 , if  n  is even 3 n + 1 , if  n  is odd f(n) = \begin{cases} n/2, & \text {if $n$ is even}\\ 3n+1, & \text {if $n$ is odd} \end{cases}f(n)={n/2,3n+1,if n is evenif n is odd

方程组

$$
\left\{
\begin{array}{c}
	a_1x+b_1y+c_1z=d_1\\
	a_2x+b_2y+c_2z=d_2\\
	a_3x+b_3y+c_3z=d_3
\end{array}
\right. # 注意right后面有个小数点
$$

{ a 1 x + b 1 y + c 1 z = d 1 a 2 x + b 2 y + c 2 z = d 2 a 3 x + b 3 y + c 3 z = d 3 \left\{ \begin{array}{c} a_1x+b_1y+c_1z=d_1\\ a_2x+b_2y+c_2z=d_2\\ a_3x+b_3y+c_3z=d_3 \end{array} \right.a1x+b1y+c1z=d1a2x+b2y+c2z=d2a3x+b3y+c3z=d3

案例

线性模型

$$h(\theta)=\sum_{j=0}^n \theta_j x_j$$

h ( θ ) = ∑ j = 0 n θ j x j h(\theta)=\sum_{j=0}^n \theta_j x_jh(θ)=j=0nθjxj

均方误差

$$J(\theta) = \frac{1}{2m} \sum_{i=0}^m (y^i-h_\theta (x^i))^2$$

J ( θ ) = 1 2 m ∑ i = 0 m ( y i − h θ ( x i ) ) 2 J(\theta) = \frac{1}{2m} \sum_{i=0}^m (y^i-h_\theta (x^i))^2J(θ)=2m1i=0m(yihθ(xi))2

批量梯度下降

$$
\frac{\partial J(\theta)}{\partial\theta_j} = -\frac{1}{m}\sum_{i=0}^m (y^i - h_\theta (x^i))x^i_j
$$

∂ J ( θ ) ∂ θ j = − 1 m ∑ i = 0 m ( y i − h θ ( x i ) ) x j i \frac{\partial J(\theta)}{\partial\theta_j} = -\frac{1}{m}\sum_{i=0}^m (y^i - h_\theta (x^i))x^i_jθjJ(θ)=m1i=0m(yihθ(xi))xji

推导过程:

$$
\begin{align}
\frac{\partial J(\theta)}{\partial \theta_j}
& = - \frac{1}{m} \sum_{i=0}^m (y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(y^i-h_\theta(x^i))\\
& = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(\sum_{j=0}^n\theta_j x^i_j - y^i)\\
& = - \frac{1}{m}\sum_{i=0}^m(y^i-h_\theta (x^i))x^i_j
\end{align}
$$

∂ J ( θ ) ∂ θ j = − 1 m ∑ i = 0 m ( y i − h θ ( x i ) ) ∂ ∂ θ j ( y i − h θ ( x i ) ) = − 1 m ∑ i = 0 m ( y i − h θ ( x i ) ) ∂ ∂ θ j ( ∑ j = 0 n θ j x j i − y i ) = − 1 m ∑ i = 0 m ( y i − h θ ( x i ) ) x j i \begin{aligned} \frac{\partial J(\theta)}{\partial \theta_j} & = - \frac{1}{m} \sum_{i=0}^m (y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(y^i-h_\theta(x^i))\\ & = -\frac{1}{m}\sum_{i=0}^m(y^i-h_\theta(x^i))\frac{\partial}{\partial\theta_j}(\sum_{j=0}^n\theta_j x^i_j - y^i)\\ & = - \frac{1}{m}\sum_{i=0}^m(y^i-h_\theta (x^i))x^i_j \end{aligned}θjJ(θ)=m1i=0m(yihθ(xi))θj(yihθ(xi))=m1i=0m(yihθ(xi))θj(j=0nθjxjiyi)=m1i=0m(yihθ(xi))xji
CSDN使用的是KaTeX(latex的渲染器),不支持align,但可以用aligned达到同样的目的

引用:

https://www.cnblogs.com/Sinte-Beuve/p/6160905.html
https://blog.csdn.net/happyday_d/article/details/83715440


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