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-rw-r--r--macros/latex/contrib/thuthesis/data/appendix-survey.tex7
1 files changed, 3 insertions, 4 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