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-rw-r--r--macros/latex/contrib/thuthesis/data/appendix-survey.tex7
-rw-r--r--macros/latex/contrib/thuthesis/data/chap01.tex18
2 files changed, 10 insertions, 15 deletions
diff --git a/macros/latex/contrib/thuthesis/data/appendix-survey.tex b/macros/latex/contrib/thuthesis/data/appendix-survey.tex
index 5b6e24a896..a3c5ef6205 100644
--- a/macros/latex/contrib/thuthesis/data/appendix-survey.tex
+++ b/macros/latex/contrib/thuthesis/data/appendix-survey.tex
@@ -32,14 +32,13 @@ as follows,
which maximizes a real-valued function $f$ of
$x=(x_1,x_2,\cdots,x_n)$ subject to a set of constraints.
-\newcommand\Real{\mathbf{R}}
\newtheorem{mpdef}{Definition}[chapter]
\begin{mpdef}
In SOP, we call $x$ a decision vector, and
$x_1,x_2,\cdots,x_n$ decision variables. The function
$f$ is called the objective function. The set
\begin{equation*}
-S=\left\{x\in\Real^n\bigm|g_j(x)\le 0,\,j=1,2,\cdots,p\right\}
+S=\left\{x\in\real^n\bigm|g_j(x)\le 0,\,j=1,2,\cdots,p\right\}
\end{equation*}
is called the feasible set. An element $x$ in $S$ is called a
feasible solution.
@@ -134,8 +133,8 @@ concerned with analyzing the structure of problems.
\end{figure}
Now we consider a nonlinear programming which is confronted solely with
-maximizing a real-valued function with domain $\Real^n$. Whether derivatives are
-available or not, the usual strategy is first to select a point in $\Real^n$ which
+maximizing a real-valued function with domain $\real^n$. Whether derivatives are
+available or not, the usual strategy is first to select a point in $\real^n$ which
is thought to be the most likely place where the maximum exists. If there is no
information available on which to base such a selection, a point is chosen at
random. From this first point an attempt is made to construct a sequence of
diff --git a/macros/latex/contrib/thuthesis/data/chap01.tex b/macros/latex/contrib/thuthesis/data/chap01.tex
index 6c2e757db2..2dfbcf4ec0 100644
--- a/macros/latex/contrib/thuthesis/data/chap01.tex
+++ b/macros/latex/contrib/thuthesis/data/chap01.tex
@@ -58,10 +58,9 @@
\subsection{基本表格}
\label{sec:basictable}
-模板中关于表格的宏包有三个:\pkg{booktabs}、\pkg{array} 和 \pkg{longtable},命
-令有一个 \cs{hlinewd}。三线表可以用 \pkg{booktabs} 提供
-的 \cs{toprule}、\cs{midrule} 和 \cs{bottomrule}。它们与 \pkg{longtable} 能很好的
-配合使用。如果表格比较简单的话可以直接用命令 \cs{hlinewd}\marg{width} 控制。
+模板中关于表格的宏包有三个:\pkg{booktabs}、\pkg{array} 和 \pkg{longtable}。
+三线表可以用 \pkg{booktabs} 提供 的 \cs{toprule}、\cs{midrule} 和 \cs{bottomrule}。
+它们与 \pkg{longtable} 能很好的配合使用。
\begin{table}[htb]
\centering
\begin{threeparttable}[b]
@@ -105,7 +104,7 @@
\begin{minipage}[t]{0.8\textwidth}
\begin{tabularx}{\linewidth}{|l|X|X|X|X|}
\hline
- \multirow{2}*{\diagbox[width=5em]{x}{y}} & \multicolumn{2}{c|}{First Half} & \multicolumn{2}{c|}{Second Half}\\\cline{2-5}
+ \multirow{2}*{} & \multicolumn{2}{c|}{First Half} & \multicolumn{2}{c|}{Second Half}\\\cline{2-5}
& 1st Qtr &2nd Qtr&3rd Qtr&4th Qtr \\ \hline
East$^{*}$ & 20.4& 27.4& 90& 20.4 \\
West$^{**}$ & 30.6 & 38.6 & 34.6 & 31.6 \\ \hline
@@ -116,9 +115,8 @@
\end{minipage}
\end{table}
-此外,表~\ref{tab:tabexamp1} 同时还演示了另外两个功能:1)通过 \pkg{tabularx} 的
- \texttt{|X|} 扩展实现表格自动放大;2)通过命令 \cs{diagbox} 在表头部分
-插入反斜线。
+此外,表~\ref{tab:tabexamp1} 同时还演示了通过 \pkg{tabularx} 的
+ \texttt{|X|} 扩展实现表格自动放大功能。
为了使我们的例子更接近实际情况,我会在必要的时候插入一些“无关”文字,以免太多图
表同时出现,导致排版效果不太理想。第一个出场的当然是我的最爱:风流潇洒、骏马绝尘、
@@ -405,7 +403,6 @@ x&\equiv y+1\pod{m^2}
乾,反复道也。或跃在渊,进无咎也。飞龙在天,大人造也。亢龙有悔,盈不可久也。用九,
天德不可为首也。   
-\newcommand\dif{\mathop{}\!\mathrm{d}}
\begin{lemma}
《猫和老鼠》是我最爱看的动画片。
\begin{multline*}%\tag*{[a]} % 这个不出现在索引中
@@ -540,9 +537,8 @@ p(y|\vx) = \frac{p(\vx,y)}{p(\vx)}=
\end{equation}
论文里面公式越多,\TeX{} 就越 happy。再看一个 \pkg{amsmath} 的例子:
-\newcommand{\envert}[1]{\left\lvert#1\right\rvert}
\begin{equation}\label{detK2}
-\det\mK(t=1,t_1,\dots,t_n)=\sum_{I\in\vn}(-1)^{\envert{I}}
+\det\mK(t=1,t_1,\dots,t_n)=\sum_{I\in\vn}(-1)^{\abs{I}}
\prod_{i\in I}t_i\prod_{j\in I}(D_j+\lambda_jt_j)\det\mA
^{(\lambda)}(\overline{I}|\overline{I})=0.
\end{equation}