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author | Karl Berry <karl@freefriends.org> | 2020-07-09 22:32:27 +0000 |
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committer | Karl Berry <karl@freefriends.org> | 2020-07-09 22:32:27 +0000 |
commit | 0799681b5ebca9c800642958339f0a2905b5b6fe (patch) | |
tree | 12a0115340557129ba3be1f2e611b5a449d738ab /Master/texmf-dist/doc/latex/thuthesis/data/appendix-survey.tex | |
parent | 1efae57dfac93679c71c3be5a5257e1bb4e2985a (diff) |
thuthesis (10jul20)
git-svn-id: svn://tug.org/texlive/trunk@55792 c570f23f-e606-0410-a88d-b1316a301751
Diffstat (limited to 'Master/texmf-dist/doc/latex/thuthesis/data/appendix-survey.tex')
-rw-r--r-- | Master/texmf-dist/doc/latex/thuthesis/data/appendix-survey.tex | 7 |
1 files changed, 3 insertions, 4 deletions
diff --git a/Master/texmf-dist/doc/latex/thuthesis/data/appendix-survey.tex b/Master/texmf-dist/doc/latex/thuthesis/data/appendix-survey.tex index 5b6e24a896f..a3c5ef6205a 100644 --- a/Master/texmf-dist/doc/latex/thuthesis/data/appendix-survey.tex +++ b/Master/texmf-dist/doc/latex/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 |