Basic linear programming

\begin{equation}\label{equation}
  \begin{aligned}
  \mbox{max} \quad &  x \\
  \mbox{s.t.} \quad &  x  \\
  & x \geq 0, \mbox{integer}\quad \mbox{for}~ i=1,\ldots,m,\\
\end{aligned}
\end{equation}

Algorithm

\begin{algorithm}[H]\label{algo}
  \caption{The algorithm}
    \begin{description}
    \item[Step 1.] 
    \item[Step 2.] 
    \item[Step 3.] 
    \item[Step 4.] 
    \item[Step 5.]
   \end{description}
  \end{algorithm}

\vspace{5pt}

Several lines

\begin{align*}
  & 0 \leq \alpha_1 \leq 1,\\ 
  & 0 \leq \alpha_2 \leq \alpha_1 + 1, \\
  & \cdots, \\
  & 0 \leq \alpha_m \leq \alpha_{m-1} + 1
\end{align*}

Big matrix

$$
\mathbf{W}=\left[\begin{array}{ccccc}
-1 & 1 & \ldots & & 0 \\
& \ddots & \ddots & & \vdots \\
& & & & 1 \\
0 & & & & -1
\end{array}\right]_{m \times m}
$$

Two-figure

\begin{figure}[h]
  \begin{minipage}[t]{0.5\textwidth}
  \centering
  \includegraphics[width=7cm]{Figures/1.pdf}
  \caption{fig1}
  \end{minipage}
  \begin{minipage}[t]{0.5\textwidth}
  \centering
  \includegraphics[width=7cm]{Figures/2.pdf}
  \caption{fig2}
  \end{minipage}
\end{figure}

Table

\begin{table}[ht]
  \begin{tabular}{l|l|l|l|l}
  \hline
  \# of &   & \# of  & \# of  &  \\
  2  & 50  & 1 & 1 & 1.26 \\
  3  & 50  & 4 & 2 & 6.3 \\
  4  & 50  & 7 & 3 & 16.1 \\
  5  & 50  & 5 & 5 & 9.6 \\
  6  & 50  & 5 & 4 & 9.7 \\
  7  & 50  & 4 & 4 & 6.5 \\
  8  & 50 & 4 & 3 & 6.2 \\
  9  & 50 & 3 & 1 & 4 \\
  10 & 50 & 3 & 1 & 4.2 \\
  \hline
  \end{tabular}
\end{table}

other shortcut

\p{x}{y}

\newcommand{\p}[2]{\frac{\partial#1}{\partial#2}}

\newcommand{\p}[2]{\ensuremath{\frac{\partial#1}{\partial#2}}}

\p(x)(y)

\def\p(#1)(#2){\ensuremath{\frac{\partial #1}{\partial #2}}}

but this method is not robust.