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| author | Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)> | 2013-07-03 20:05:28 -0700 |
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| committer | Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)> | 2013-07-03 20:05:28 -0700 |
| commit | 5db33d6a133669cb876f1b4da3c1c1c6fedd0d19 (patch) | |
| tree | 6e74ed072f2b7e33fb7257aa494fb3c775992edb /problem.tex | |
| parent | 297af7c33e8cc93494cde48212b502f40adc9546 (diff) | |
| download | recommendation-5db33d6a133669cb876f1b4da3c1c1c6fedd0d19.tar.gz | |
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Diffstat (limited to 'problem.tex')
| -rw-r--r-- | problem.tex | 4 |
1 files changed, 1 insertions, 3 deletions
diff --git a/problem.tex b/problem.tex index bb69120..a00e2f6 100644 --- a/problem.tex +++ b/problem.tex @@ -93,15 +93,13 @@ Each experiment is associated with a cost $c_i\in\reals_+$. Moreover, the experi The cost $c_i$ can capture, \emph{e.g.}, the amount the subject $i$ deems sufficient to incentivize her participation in the experiment. In the full-information case, the experiment costs are common knowledge; as such, the optimization problem that the experimenter wishes to solve is: -\begin{center} -\textsc{ExperimentalDesignProblem} (EDP) +\medskip\\\hspace*{\stretch{1}}\textsc{ExperimentalDesignProblem} (\EDP)\hspace*{\stretch{1}} \begin{subequations} \begin{align} \text{Maximize}\quad V(S) &= \log\det(I_d+\T{X_S}X_S) \label{modified} \\ \text{subject to}\quad \sum_{i\in S} c_i&\leq B \end{align}\label{edp} \end{subequations} -\end{center} We denote by \begin{equation}\label{eq:non-strategic} OPT = \max_{S\subseteq\mathcal{N}} \Big\{V(S) \;\Big| \; |
