From 58c72935ed45da7f7dea0e28009219e5c5ba919c Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Sat, 3 Nov 2012 01:31:46 +0100
Subject: It's Chen, not Bei...

---
 main.tex | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'main.tex')

diff --git a/main.tex b/main.tex
index 24c7629..e2b8335 100644
--- a/main.tex
+++ b/main.tex
@@ -36,7 +36,7 @@ set). It has been noted by Khuller et al. \cite{khuller} that for the maximum
 coverage problem, taking the maximum between the greedy solution and $V(i^*$)
 gives a $\frac{2e}{e-1}$ approximation ratio. In the general case, we have the
 following result from Singer \cite{singer-influence} which follows from
-Bei et al. \cite{chen}:
+Chen et al. \cite{chen}:
 
 \begin{lemma}[Singer \cite{singer-influence}]\label{lemma:greedy-bound}
 Let $S_G$ be the set computed by the greedy heuristic and let us define $i^*$:
@@ -67,10 +67,10 @@ $V(S_G)$ while maintaining the monotonicity of the allocation algorithm.
 Furthermore, it must be computable in polynomial time to keep an overall
 polynomial complexity for the allocation algorithm. If the underlying
 non-strategic optimization program \eqref{eq:non-strategic} can be solved in
-polynomial time, Bei et al. \cite{chen} prove that allocating to $i^*$ when
+polynomial time, Chen et al. \cite{chen} prove that allocating to $i^*$ when
 $V(i^*) \geq C\cdot OPT(V,\mathcal{N}\setminus\{i^*\}$ (for some constant $C$)
 and to $S_G$ otherwise yields a 8.34 approximation mechanism. For specific
-problems, Bei et al. \cite{chen} for knapsack on one hand, Singer
+problems, Chen et al. \cite{chen} for knapsack on one hand, Singer
 \cite{singer-influence} for maximum coverage on the other hand, instead compare
 $V(i^*)$ to $OPT(R_{\mathcal{N}\setminus\{i\}}, B)$, where $R_\mathcal{N}$
 denotes a fractional relaxation of the function $V$ over the set
@@ -95,7 +95,7 @@ $\lambda_i$ or to reject it with probability $1-\lambda_i$:
     P_\mathcal{N}^\lambda = \prod_{i\in S} \lambda_i
     \prod_{i\in\mathcal{N}\setminus S} 1 - \lambda_i
 \end{displaymath}
-For knapsack, Bei et al. \cite{chen} directly use the multi-linear extension as
+For knapsack, Chen et al. \cite{chen} directly use the multi-linear extension as
 the relaxation to compare $V(i^*)$ to. For maximum coverage however, the
 optimal value of the multi-linear extension cannot be computed in polynomial
 time. Thus, Singer \cite{singer-influence} introduces a second relaxation of
-- 
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