Latex写算法伪代码

科研过程中利用Latex写文章是非常方便的一件事，下面是latex的一些写伪代码的代码。

1. Code One

\documentclass[conference]{IEEEtran}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{amsmath}
\begin{document}
%% 写算法伪代码或者流程的前期准备
\renewcommand{\algorithmicrequire}{\textbf{Input:}}  % Use Input in the format of Algorithm
\renewcommand{\algorithmicensure}{\textbf{Output:}} % Use Output in the format of Algorithm

\begin{algorithm}[h]
  \caption{Conjugate Gradient Algorithm with Dynamic Step-Size Control} % 名称
  \label{alg::conjugateGradient}
  \begin{algorithmic}[1]
    \Require
      $x_0$: initial individual, i.e, state;
      $x_0$: initial solution;
      $s$: step size;
    \Ensure
      optimal $x^{*}$
    \State initial $g_0=0$ and $d_0=0$;
    \Repeat
      \State compute gradient directions $g_k=\bigtriangledown f(x_k)$;
      \State compute Polak-Ribiere parameter $\beta_k=\frac{g_k^{T}(g_k-g_{k-1})}{\parallel g_{k-1} \parallel^{2}}$;
      \State compute the conjugate directions $d_k=-g_k+\beta_k d_{k-1}$;
      \State compute the step size $\alpha_k=s/\parallel d_k \parallel_{2}$;
    \Until{($f(x_k)>f(x_{k-1})$)}
  \end{algorithmic}
\end{algorithm}
\end{document}

Result One

result One.png

2. Code Two

\documentclass[conference]{IEEEtran}
\usepackage{algorithm}
\usepackage{algorithm}
\usepackage{algorithmicx}
\usepackage{algpseudocode}
\usepackage{amsmath}
\usepackage[top=2cm, bottom=2cm, left=2cm, right=2cm]{geometry}
\begin{document}

%% 写算法伪代码或者流程的前期准备
\renewcommand{\algorithmicrequire}{\textbf{Input:}}  % Use Input in the format of Algorithm
\renewcommand{\algorithmicensure}{\textbf{Output:}} % Use Output in the format of Algorithm

\begin{algorithm}[h]
  \caption{Pseudocode of Simulated Annealing Algorithm} % 名称
  \begin{algorithmic}[1]
    \Require
      $x_0$: initial individual or state;
      $T_0$: a high enough initial temperature;
      $T_{min}$: the lowest limit of temperature;
    \Ensure
       optimal state or approximate optimal state;
       \State set $x_0 = x_{best}$, compute initial energy function $E(x_0)$;
       \While {$T > T_{min}$}
         \For{$i = 1$; $i<n$; $i++$ }
      \State perturb current state $x_i$ for a new state $x_{new}$ and compute energy function $E(x_{new})$;
      \State compute $\Delta$ = $E(x_{new}-E(x_{(i)})$;
      \If {$\Delta$$E<0$} \State $x_{best} = x_{new}$
      \Else \State the probability $P = exp(-dE/T_{(i)})$;
      \If {$rand(0,1) < P$ }\State $x_{best} = x_{new}$
      \Else \State $x_{best} = x_{best}$
      \EndIf
     \EndIf
     \EndFor
      \State $T = T * $ $ \alpha$, where $\alpha$ is decay factor  ;
    \EndWhile
  \end{algorithmic}
\end{algorithm}

\end{document}

Result Two

Result Two.png