Latex——伪代码算法

一般在论文的写作中，需要有算法流程图，现讲述两种伪代码算法流程图的实现：algorithm,algorithme2

第一个：algorithm

%导包
\usepackage{algorithm}
\usepackage{algorithmic}

完整代码：

\documentclass{
     article}
\usepackage{
     algorithm}
\usepackage{
     algorithmic}

\begin{
     document}
	
\begin{
     algorithm}
	\renewcommand{
     \algorithmicrequire}{
     \textbf{
     Input:}}
	\renewcommand{
     \algorithmicensure}{
     \textbf{
     Output:}}
	\caption{
     Bayesian Personalized Ranking Based Latent Feature Embedding Model}
	\label{
     alg:1}
	\begin{
     algorithmic}[1]
		\REQUIRE latent dimension $K$, $G$, target predicate $p$
		\ENSURE $U^{
     p}$, $V^{
     p}$, $b^{
     p}$
		\STATE Given target predicate $p$ and entire knowledge graph $G$, construct its bipartite subgraph, $G_{
     p}$ 
		\STATE $m$ = number of subject entities in $G_{
     p}$
		\STATE $n$ = number of object entities in $G_{
     p}$ 
		\STATE Generate a set of training samples $D_{
     p} = \{
     (s_p, o^{
     +}_{
     p}, o^{
     -}_{
     p})\}$ using uniform sampling technique
		\STATE Initialize $U^{
     p}$ as size $m \times K$ matrix with $0$ mean and standard deviation $0.1$
		\STATE Initialize $V^{
     p}$ as size $n \times K$ matrix with $0$ mean and stardard deviation $0.1$
		\STATE Initialize $b^{
     p}$ as size $n \times 1$ column vector with $0$ mean and stardard deviation $0.1$
		\FORALL{
     $(s_p, o^{
     +}_{
     p}, o^{
     -}_{
     p}) \in D_{
     p}$}
		\STATE Update $U_{
     s}^{
     p}$ based on Equation~\ref{
     eq:sgd1}
		\STATE Update $V_{
     o^{
     +}}^{
     p}$ based on Equation~\ref{
     eq:sgd2}
		\STATE Update $V_{
     o^{
     -}}^{
     p}$ based on Equation~\ref{
     eq:sgd3}
		\STATE Update $b_{
     o^{
     +}}^{
     p}$ based on Equation~\ref{
     eq:sgd4}
		\STATE Update $b_{
     o^{
     -}}^{
     p}$ based on Equation~\ref{
     eq:sgd5}
		\ENDFOR
		\STATE \textbf{
     return} $U^{
     p}$, $V^{
     p}$, $b^{
     p}$
	\end{
     algorithmic}  
\end{
     algorithm}

\end{
     document}

结果如下

第二个：algorith2me

%导包
\usepackage[ruled]{algorithm2e}

完整代码

\documentclass{
     article}
\usepackage[ruled]{
     algorithm2e}

\begin{
     document}

\begin{
     algorithm}[h]
	
	\caption{
     The process of location change of fish}  
	\label{
     alg:1}
	\KwIn{
     $\rho(x,y,0),\beta,v_{
     max},u_{
     min},u_{
     max}$}
	\KwOut{
     $\rho(x,y,50)$}  
	
	\For{
     t=1 to 50}{
     
		The random distractor $\epsilon_t$ can be get in the process of identification of variance \newline
		The dispersed $u(x, y, t)$ can be predicted based on the model ARIMA(1,1,0) and the $u(x, y, t 1)$ \newline
		The continuous $u(x, y, t)$ can be get based on the linear interposition of value of the dispersed $u(x, y, t)$\newline
		The continuous $\nabla u$ can be identified based on the equation (11) \newline
		The continuous $v(x, y, t)$ can be calculated based on the equation (12) \newline
		The location change of each fish can be calculated based on equation (7) \newline
		The $\rho_t(x, y)$ of each fish can be refreshed based on the equation (8) \newline
	}
\end{
     algorithm}  

\end{
     document}  

结果如下

关于 algorithm2e的补充

\usepackage[ruled,linesnumbered]{algorithm2e}

• ruled 让标题显示在上方，否则将显示在下方
• linesnumbered 在算法流程图内显示行号
• 可添加 boxed，让算法排版时插入在一个盒子里

展示效果如下：

另外补充：

代码	含义
\;	在行末添加分号，并自动换行
\caption{}	插入标题
\KwIn{输入消息}	效果为：“input:输入消息”
KwOut{输出消息}	效果为：“Out:输出消息”
KwData{输入消息}	效果为：“Data:输入消息”
KwResult{输出消息}	效果为：“Result:输出消息”
\For{条件}{循环语句}	for 条件 do 循环语句 end
\If{条件}{肯定语句}	if 条件 then 肯定语句 end
While{条件}{循环语句}	while 条件 then 循环语句 end
\tcc{注释}	/*注释*/
\tcp{注释}	//注释
\elf{条件}{肯定语句}{否定语句}	if 条件 then 肯定语句 else 否定语句 end

如果有问题，欢迎给我留言