Update to v3.0.4

Author: YangLaTeX

back/appendix01.tex

@@ -1,104 +1,32 @@
 % !TEX root = ../main.tex
 
 % 附录1
-\chapter{外文资料原文}
-\label{cha:engorg}
+\chapter{外文资料的调研阅读报告或书面翻译}
 
-\title{The title of the English paper}
+\title{英文资料的中文标题}
 
-\textbf{Abstract:} As one of the most widely used techniques in operations
-research, \emph{ mathematical programming} is defined as a means of maximizing a
-quantity known as \emph{bjective function}, subject to a set of constraints
-represented by equations and inequalities. Some known subtopics of mathematical
-programming are linear programming, nonlinear programming, multiobjective
-programming, goal programming, dynamic programming, and multilevel
-programming$^{[1]}$.
+{\heiti 摘要：} 本章为外文资料翻译内容。如果有摘要可以直接写上来，这部分好像没有
+明确的规定。
 
-It is impossible to cover in a single chapter every concept of mathematical
-programming. This chapter introduces only the basic concepts and techniques of
-mathematical programming such that readers gain an understanding of them
-throughout the book$^{[2,3]}$.
-
-
-\section{Single-Objective Programming}
-The general form of single-objective programming (SOP) is written
-as follows,
-\begin{equation}\tag*{(123)} % 如果附录中的公式不想让它出现在公式索引中，那就请
-                             % 用 \tag*{xxxx}
-\left\{\begin{array}{l}
-\max \,\,f(x)\\[0.1 cm]
-\mbox{subject to:} \\ [0.1 cm]
-\qquad g_j(x)\le 0,\quad j=1,2,\cdots,p
-\end{array}\right.
+\section{单目标规划}
+北冥有鱼，其名为鲲。鲲之大，不知其几千里也。化而为鸟，其名为鹏。鹏之背，不知其几
+千里也。怒而飞，其翼若垂天之云。是鸟也，海运则将徙于南冥。南冥者，天池也。
+\begin{equation}\tag*{(123)}
+ p(y|\mathbf{x}) = \frac{p(\mathbf{x},y)}{p(\mathbf{x})}=
+\frac{p(\mathbf{x}|y)p(y)}{p(\mathbf{x})}
 \end{equation}
-which maximizes a real-valued function $f$ of
-$x=(x_1,x_2,\cdots,x_n)$ subject to a set of constraints.
 
-\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}\tag*{(456)} % 这里同理，其它不再一一指定。
-S=\left\{x\in\Re^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.
-\end{mpdef}
+吾生也有涯，而知也无涯。以有涯随无涯，殆已！已而为知者，殆而已矣！为善无近名，为
+恶无近刑，缘督以为经，可以保身，可以全生，可以养亲，可以尽年。
 
-\newtheorem{mpdefop}[mpdef]{Definition}
-\begin{mpdefop}
-A feasible solution $x^*$ is called the optimal
-solution of SOP if and only if
-\begin{equation}
-f(x^*)\ge f(x)
-\end{equation}
-for any feasible solution $x$.
-\end{mpdefop}
-
-One of the outstanding contributions to mathematical programming was known as
-the Kuhn-Tucker conditions\ref{eq:ktc}. In order to introduce them, let us give
-some definitions. An inequality constraint $g_j(x)\le 0$ is said to be active at
-a point $x^*$ if $g_j(x^*)=0$. A point $x^*$ satisfying $g_j(x^*)\le 0$ is said
-to be regular if the gradient vectors $\nabla g_j(x)$ of all active constraints
-are linearly independent.
-
-Let $x^*$ be a regular point of the constraints of SOP and assume that all the
-functions $f(x)$ and $g_j(x),j=1,2,\cdots,p$ are differentiable. If $x^*$ is a
-local optimal solution, then there exist Lagrange multipliers
-$\lambda_j,j=1,2,\cdots,p$ such that the following Kuhn-Tucker conditions hold,
-\begin{equation}
-\label{eq:ktc}
-\left\{\begin{array}{l}
-    \nabla f(x^*)-\sum\limits_{j=1}^p\lambda_j\nabla g_j(x^*)=0\\[0.3cm]
-    \lambda_jg_j(x^*)=0,\quad j=1,2,\cdots,p\\[0.2cm]
-    \lambda_j\ge 0,\quad j=1,2,\cdots,p.
-\end{array}\right.
-\end{equation}
-If all the functions $f(x)$ and $g_j(x),j=1,2,\cdots,p$ are convex and
-differentiable, and the point $x^*$ satisfies the Kuhn-Tucker conditions
-(\ref{eq:ktc}), then it has been proved that the point $x^*$ is a global optimal
-solution of SOP.
-
-\subsection{Linear Programming}
-\label{sec:lp}
-
-If the functions $f(x),g_j(x),j=1,2,\cdots,p$ are all linear, then SOP is called
-a {\em linear programming}.
-
-The feasible set of linear is always convex. A point $x$ is called an extreme
-point of convex set $S$ if $x\in S$ and $x$ cannot be expressed as a convex
-combination of two points in $S$. It has been shown that the optimal solution to
-linear programming corresponds to an extreme point of its feasible set provided
-that the feasible set $S$ is bounded. This fact is the basis of the {\em simplex
-  algorithm} which was developed by Dantzig as a very efficient method for
-solving linear programming.
+\subsection{线性规划}
+庖丁为文惠君解牛，手之所触，肩之所倚，足之所履，膝之所倚，砉然响然，奏刀騞然，莫
+不中音，合于桑林之舞，乃中经首之会。
 \begin{table}[ht]
 \centering
   \centering
-  \caption*{Table~1\hskip1em This is an example for manually numbered table, which
-    would not appear in the list of tables}
-  \label{tab:badtabular2}
+  \caption*{表~1\hskip1em 这是手动编号但不出现在索引中的一个表格例子}
+  \label{tab:badtabular3}
   \begin{tabular}[c]{|m{1.5cm}|c|c|c|c|c|c|}\hline
     \multicolumn{2}{|c|}{Network Topology} & \# of nodes &
     \multicolumn{3}{c|}{\# of clients} & Server \\\hline
@@ -112,64 +40,33 @@ \subsection{Linear Programming}
 \end{tabular}
 \end{table}
 
-Roughly speaking, the simplex algorithm examines only the extreme points of the
-feasible set, rather than all feasible points. At first, the simplex algorithm
-selects an extreme point as the initial point. The successive extreme point is
-selected so as to improve the objective function value. The procedure is
-repeated until no improvement in objective function value can be made. The last
-extreme point is the optimal solution.
-
-\subsection{Nonlinear Programming}
-
-If at least one of the functions $f(x),g_j(x),j=1,2,\cdots,p$ is nonlinear, then
-SOP is called a {\em nonlinear programming}.
-
-A large number of classical optimization methods have been developed to treat
-special-structural nonlinear programming based on the mathematical theory
-concerned with analyzing the structure of problems.
+文惠君曰：“嘻，善哉！技盖至此乎？”庖丁释刀对曰：“臣之所好者道也，进乎技矣。始臣之
+解牛之时，所见无非全牛者；三年之后，未尝见全牛也；方今之时，臣以神遇而不以目视，
+官知止而神欲行。依乎天理，批大郤，导大窾，因其固然。技经肯綮之未尝，而况大坬乎！
+良庖岁更刀，割也；族庖月更刀，折也；今臣之刀十九年矣，所解数千牛矣，而刀刃若新发
+于硎。彼节者有间而刀刃者无厚，以无厚入有间，恢恢乎其于游刃必有余地矣。是以十九年
+而刀刃若新发于硎。虽然，每至于族，吾见其难为，怵然为戒，视为止，行为迟，动刀甚微，
+謋然已解，如土委地。提刀而立，为之而四顾，为之踌躇满志，善刀而藏之。”
 
-Now we consider a nonlinear programming which is confronted solely with
-maximizing a real-valued function with domain $\Re^n$.  Whether derivatives are
-available or not, the usual strategy is first to select a point in $\Re^n$ 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
-points, each of which yields an improved objective function value over its
-predecessor. The next point to be added to the sequence is chosen by analyzing
-the behavior of the function at the previous points. This construction continues
-until some termination criterion is met. Methods based upon this strategy are
-called {\em ascent methods}, which can be classified as {\em direct methods},
-{\em gradient methods}, and {\em Hessian methods} according to the information
-about the behavior of objective function $f$. Direct methods require only that
-the function can be evaluated at each point. Gradient methods require the
-evaluation of first derivatives of $f$. Hessian methods require the evaluation
-of second derivatives. In fact, there is no superior method for all
-problems. The efficiency of a method is very much dependent upon the objective
-function.
+文惠君曰：“善哉！吾闻庖丁之言，得养生焉。”
 
-\subsection{Integer Programming}
 
-{\em Integer programming} is a special mathematical programming in which all of
-the variables are assumed to be only integer values. When there are not only
-integer variables but also conventional continuous variables, we call it {\em
-  mixed integer programming}. If all the variables are assumed either 0 or 1,
-then the problem is termed a {\em zero-one programming}. Although integer
-programming can be solved by an {\em exhaustive enumeration} theoretically, it
-is impractical to solve realistically sized integer programming problems. The
-most successful algorithm so far found to solve integer programming is called
-the {\em branch-and-bound enumeration} developed by Balas (1965) and Dakin
-(1965). The other technique to integer programming is the {\em cutting plane
-  method} developed by Gomory (1959).
+\subsection{非线性规划}
+孔子与柳下季为友，柳下季之弟名曰盗跖。盗跖从卒九千人，横行天下，侵暴诸侯。穴室枢
+户，驱人牛马，取人妇女。贪得忘亲，不顾父母兄弟，不祭先祖。所过之邑，大国守城，小
+国入保，万民苦之。孔子谓柳下季曰：“夫为人父者，必能诏其子；为人兄者，必能教其弟。
+若父不能诏其子，兄不能教其弟，则无贵父子兄弟之亲矣。今先生，世之才士也，弟为盗
+跖，为天下害，而弗能教也，丘窃为先生羞之。丘请为先生往说之。”
 
-\hfill\textit{Uncertain Programming\/}\quad(\textsl{BaoDing Liu, 2006.2})
+柳下季曰：“先生言为人父者必能诏其子，为人兄者必能教其弟，若子不听父之诏，弟不受
+兄之教，虽今先生之辩，将奈之何哉？且跖之为人也，心如涌泉，意如飘风，强足以距敌，
+辩足以饰非。顺其心则喜，逆其心则怒，易辱人以言。先生必无往。”
 
-\section*{References}
-\noindent{\itshape NOTE: These references are only for demonstration. They are
-  not real citations in the original text.}
+孔子不听，颜回为驭，子贡为右，往见盗跖。
 
-\begin{translationbib}
-\item Donald E. Knuth. The \TeX book. Addison-Wesley, 1984. ISBN: 0-201-13448-9
-\item Paul W. Abrahams, Karl Berry and Kathryn A. Hargreaves. \TeX\ for the
-  Impatient. Addison-Wesley, 1990. ISBN: 0-201-51375-7
-\item David Salomon. The advanced \TeX book.  New York : Springer, 1995. ISBN:0-387-94556-3
-\end{translationbib}
+\subsection{整数规划}
+盗跖乃方休卒徒大山之阳，脍人肝而餔之。孔子下车而前，见谒者曰：“鲁人孔丘，闻将军
+高义，敬再拜谒者。”谒者入通。盗跖闻之大怒，目如明星，发上指冠，曰：“此夫鲁国之
+巧伪人孔丘非邪？为我告之：尔作言造语，妄称文、武，冠枝木之冠，带死牛之胁，多辞缪
+说，不耕而食，不织而衣，摇唇鼓舌，擅生是非，以迷天下之主，使天下学士不反其本，妄
+作孝弟，而侥幸于封侯富贵者也。子之罪大极重，疾走归！不然，我将以子肝益昼餔之膳。”