LaTeX 算法代码排版 --latex2e范例总结

LaTeX 写作： 算法代码排版 --latex2e范例总结

 

latex2e 宏包的使用范例：

 

• \usepackage[ruled]{algorithm2e}                                     %算法排版样式1
• \usepackage[ruled,vlined]{algorithm2e}                          %算法排版样式2
• \usepackage[linesnumbered,boxed]{algorithm2e}        %算法排版样式3

 

使用上述代码中三种不同的宏包编译后的代码排版样式预览：

样式一：

样式二：

样式三：

 

latex代码排版.tex文件： 

 

\documentclass{article}

 \usepackage[ruled]{algorithm2e}                 %算法排版样式1
%\usepackage[ruled,vlined]{algorithm2e}          %算法排版样式2
%\usepackage[linesnumbered,boxed]{algorithm2e}   %算法排版样式3
    
 \begin{document}
   
    How to use the algorithm2e in \LaTeX ~ file.
        
    Examples: 

%    ------------------------------Example - 1---------------------------------------------
        \begin{algorithm}[H]
            \caption{How to write algorithms}
            \KwIn{this text}
            \KwOut{how to write algorithm with \LaTeX2e }
            
            initialization\;
            \While{not at end of this document}{
                read current\;
                \eIf{understand}
                {
                        go to next section\;
                        current section becomes this one\;
                }
                {
                    go back to the beginning of current section\;
                }
        }
    \end{algorithm}
    
%    ---------------------------Example - 2------------------------------------------------
    \begin{algorithm} 
%    \SetAlgoNoLine  %去掉之前的竖线
     \caption{identifyRowContext} 
      \KwIn{$r_i$, $Backgrd(T_i)$=${T_1,T_2,\ldots ,T_n}$ and similarity threshold $\theta_r$} 
      \KwOut{$con(r_i)$} 
        $con(r_i)= \Phi$\; 
        \For{$j=1;j \le n;j \ne i$} 
        { 
            float $maxSim=0$\; 
            $r^{maxSim}=null$\; 
            \While{not end of $T_j$} 
            { 
                compute Jaro($r_i,r_m$)($r_m\in T_j$)\; 
                \If{$(Jaro(r_i,r_m) \ge \theta_r)\wedge (Jaro(r_i,r_m)\ge r^{maxSim})$} 
                { 
                    replace $r^{maxSim}$ with $r_m$\; 
                } 
            } 
            $con(r_i)=con(r_i)\cup {r^{maxSim}}$\; 
        } 
        return $con(r_i)$\; 
    \end{algorithm}
    %--------------------------------------------------------------------------------------

 some special information
    The algorithm2e LaTeX package conflicts with several others over the use of the algorithm identifier.  A common indicator is something like this message: 
To resolve the issues, simply put the following just before the inclusion of the algorithm2e package:

%\makeatletter
%\newif\if@restonecol
%\makeatother
%\let\algorithm\relax
%\let\endalgorithm\relax

\end{document}

 中文latex模式下，更改Input为输入，更改Output为输出的方法： 在算法内部插入、

 

\SetKwInOut{KIN}{输入}

\SetKwInOut{KOUT}{输出}

 

用我们定义的新的宏名“\KIN” 在算法开头相应位置替换掉自带的\KwInput，用"\KOUT" 替换掉\KwOutput 即可，实现中文的输入和输出.

具体事例如下：

 

\renewcommand{\algorithmcfname}{算法}
\begin{algorithm}[H]
%\SetAlgoNoLine
 4 \SetKwInOut{KIN}{输入}
 5 \SetKwInOut{KOUT}{输出}
%\BlankLine  %空一行
    \caption{标准DE算法 }
    \label{DE_algo} %
    \KIN{Population: $ M$; Dimension: $ D $; Genetation: $ T $ }
    \KOUT{The best vector (solution)  $ \varDelta $ }
    $ t \leftarrow 1 (initialization) $\;
    \For{$i=1$ to $ M $    }
    {\For{$j=1$ to $ D $}
        {
            $  {x}_{i,t}^j=x_{min}^j + rand(0,1)\cdotp (x_{max}^j-x_{min}^j) $\;
        }
    }        
    %--------------------------------------------------    
\While{$(|f(\varDelta)| \geq\varepsilon )$      or     $(t \leq T )$}
    {
        \For{$ i=1$  to $M$}
        {
\emph{$ \blacktriangleright $ (Mutation and Crossover)}\\            
%\textit{ $ \blacktriangleright $ (Mutation and Crossover) }\\
            \For{$j=1$ to $ D $}
            {    
                $ v_{i,t}^j =Mutation(x_{i,t}^j)$\;    
                $ u_{i,t}^j =Crossover(x_{i,t}^j,v_{i,t}^j)$\;
            }
\emph{$ \blacktriangleright $ (Greedy Selection)}\\
            %\textit{ $ \blacktriangleright $ (Greedy Selection) }\\
            \eIf{$ f(\textbf{u}_{i,t}) <  f(\textbf{x}_{i,t}) $}
            {
                $  \textbf{x}_{i,t} \leftarrow\textbf{u}_{i,t}$\;    
                \If{$  f(\textbf{x}_{i,t}) < f(\varDelta)$}
                {
                    $ \varDelta \leftarrow \textbf{x}_{i,t}$ \;
                }
            }
            {
                $  \textbf{x}_{i,t} \leftarrow \textbf{x}_{i,t} $\;
            }
        }
        $ t \leftarrow t+1 $\;    
    }    %While        
    \Return the best vector  $\varDelta$\;
\end{algorithm}

 

 

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