Realize a LaTeX Pseudo-code Block Using LaTeX algorithm
and algpseudocode
Package
Jun. 02, 2024
Here is a LaTeX pseudo-code block example by LaTeX algorithm
and algpseudocode
package from the paper, Wasserstein Generative Adversarial Networks12:
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\documentclass[a4paper,11pt]{article}
%\documentclass[a4paper,twocolumn]{article}
\usepackage{amsmath,amsfonts}
\usepackage{algorithm,algpseudocode}
\newcommand\PP{\mathbb{P}}
\begin{document}
\begin{algorithm}[t!]
\caption{WGAN, our proposed algorithm. All experiments in the paper
used the default values $\alpha = 0.00005$, $c = 0.01$, $m=64$, $n_{\text{critic}}=5$.}\label{algo::wgan}
\begin{algorithmic}[1]
\Require: $\alpha$, the learning rate. $c$, the clipping parameter. $m$, the batch size. $n_{\text{critic}}$, the number of iterations of the critic per generator iteration.
\Require: $w_0$, initial critic parameters. $\theta_0$, initial generator's parameters.
\While{$\theta$ has not converged}
\For{$t = 0, ..., n_{\text{critic}}$}
\State Sample $\{x^{(i)}\}_{i=1}^m \sim \PP_r$ a batch from the real data.
\State Sample $\{z^{(i)}\}_{i=1}^m \sim p(z)$ a batch of prior samples.
\State $g_w \gets \nabla_w \left[\frac{1}{m}\sum_{i=1}^m f_w(x^{(i)}) - \frac{1}{m} \sum_{i=1}^m f_w(g_\theta(z^{(i)})) \right]$
\State $w \gets w + \alpha \cdot \text{RMSProp}(w, g_w) $
\State $w \gets \text{clip}(w, -c, c) $
\EndFor
\State Sample $\{z^{(i)}\}_{i=1}^m \sim p(z)$ a batch of prior samples.
\State $g_\theta \gets -\nabla_\theta \frac{1}{m} \sum_{i=1}^m f_w(g_\theta(z^{(i)}))$
\State $\theta \gets \theta - \alpha \cdot \text{RMSProp}(\theta, g_\theta)$
\EndWhile
\end{algorithmic}
\end{algorithm}
\end{document}
where the option [1]
of algorithmic
environment is to display the line numbers3. If we delete it, so we have:
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% ...
\begin{algorithmic}
% ...
\end{algorithmic}
% ...
and if we use other certain number, like [7]
for example, instead, we have:
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% ...
\begin{algorithmic}[7]
% ...
\end{algorithmic}
% ...
References
-
Arjovsky, Martin, Soumith Chintala, and Léon Bottou. “Wasserstein generative adversarial networks.” International conference on machine learning. PMLR, 2017, https://arxiv.org/abs/1701.07875. ˄