From 5db33d6a133669cb876f1b4da3c1c1c6fedd0d19 Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Wed, 3 Jul 2013 20:05:28 -0700
Subject: approx

---
 problem.tex | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

(limited to 'problem.tex')

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| \;
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