LaTeX实战经验：如何写算法

LaTeX中实现算法的呈现主要有两种方式：

• 使用宏包algorithm2e, 这个宏包有很多可选项进行设定。
• 使用宏包algorithm 与 algorithmic, 

使用宏包algorithm2e：

usepackage[linesnumbered,boxed,ruled,commentsnumbered]{algorithm2e}%%算法包，注意设置所需可选项

 

例子:

IncMargin{1em} % 使得行号不向外突出

egin{algorithm}

    SetAlgoNoLine % 不要算法中的竖线

    SetKwInOut{Input}{ extbf{输入}}SetKwInOut{Output}{ extbf{输出}} % 替换关键词

    Input{

        \

        The observed user-item pair set $S$;\

        The feature matrix of items $F$;\

        The content features entities $A := {A^u,A^v}$;\}

    Output{

        \

        $Theta   := {Y^u,Y^v}$;\

        $W := {W^u,W^v}$;\}

    BlankLine

    initialize the model parameter $Theta$ and $W$ with uniform $left(-sqrt{6}/{k},sqrt{6}/{k} ight)$; % 分号 ; 区分一行结束

    standarized $Theta$;

    Initialize the popularity of categories $ ho$ randomly;

    Repeat

        { ext{convergence}}

        {Draw a triple $left(m,i,j ight)$ with 算法 ef{al2};

            For {each latent vector $ heta in Theta$}{

                $ heta leftarrow heta - etafrac{partial L}{partial heta}$

            }

            For {each $W^e in W$}{

                Update $W^e$ with the rule defined in Eq. ef{equ:W};

            }  

        }

    caption{Learning paramters for BPRlabel{al3}}

end{algorithm}

DecMargin{1em}

使用宏包algorithm与algorithmic

usepackage{algorithm, algorithmic}

egin{algorithm}

         enewcommand{algorithmicrequire}{ extbf{Input:}}

         enewcommand{algorithmicensure}{ extbf{Output:}}

         caption{Bayesian Personalized Ranking Based Latent Feature Embedding Model}

         label{alg:1}

         egin{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 $m imes K$ matrix with $0$ mean and standard deviation $0.1$

                   STATE Initialize $V^{p}$ as size $n imes K$ matrix with $0$ mean and stardard deviation $0.1$

                   STATE Initialize $b^{p}$ as size $n imes 1$ 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~ ef{eq:sgd1}

                   STATE Update $V_{o^{+}}^{p}$ based on Equation~ ef{eq:sgd2}

                   STATE Update $V_{o^{-}}^{p}$ based on Equation~ ef{eq:sgd3}

                   STATE Update $b_{o^{+}}^{p}$ based on Equation~ ef{eq:sgd4}

                   STATE Update $b_{o^{-}}^{p}$ based on Equation~ ef{eq:sgd5}

                   ENDFOR

                   STATE extbf{return} $U^{p}$, $V^{p}$, $b^{p}$

         end{algorithmic} 

end{algorithm}

重新定义require和ensure命令对应的关键字(此处将默认的Require/Ensure自定义为Input/Output)

enewcommand{algorithmicrequire}{ extbf{Input:}} 
enewcommand{algorithmicensure}{ extbf{Output:}}