From 50900bfc44961b87379cd2d3464b677d9f5be1ac Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Sat, 21 Sep 2013 18:16:12 -0400
Subject: Converting to LATIN format

---
 approximation.tex | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

(limited to 'approximation.tex')

diff --git a/approximation.tex b/approximation.tex
index 4978279..3e3f482 100644
--- a/approximation.tex
+++ b/approximation.tex
@@ -47,7 +47,7 @@ expectation of $V$ under the  distribution $P_\mathcal{N}^\lambda$:
     F(\lambda) 
     \defeq \mathbb{E}_{S\sim P_\mathcal{N}^\lambda}\big[V(S)\big]
   %  = \sum_{S\subseteq\mathcal{N}} P_\mathcal{N}^\lambda(S) V(S)
-= \mathbb{E}_{S\sim P_\mathcal{N}^\lambda}\left[ \log\det\left( I_d + \sum_{i\in S} x_i\T{x_i}\right) \right],\qquad \lambda\in[0,1]^n.
+= \mathbb{E}_{S\sim P_\mathcal{N}^\lambda}\left[ \log\det\left( I_d + \sum_{i\in S} x_i\T{x_i}\right) \right],\quad \lambda\in[0,1]^n.
 \end{equation}
 Function $F$ is an extension of $V$ to the domain  $[0,1]^n$, as it equals $V$ on integer inputs: $F(\id_S) = V(S)$ for all
 $S\subseteq\mathcal{N}$, where $\id_S$ denotes the indicator vector of $S$. %\citeN{pipage} have shown how to use this extension to obtain approximation guarantees for an interesting class of optimization problems through the \emph{pipage rounding} framework, which has been successfully applied in \citeN{chen, singer-influence}. 
@@ -55,9 +55,12 @@ Contrary to problems such as \textsc{Knapsack}, the
 multi-linear extension \eqref{eq:multi-linear} cannot be optimized in
 polynomial time for  the value function $V$  we study here, given by \eqref{modified}. Hence, we introduce an extension $L:[0,1]^n\to\reals$ s.t.~
 \begin{equation}\label{eq:our-relaxation}
-\quad L(\lambda) \defeq
-\log\det\left(I_d + \sum_{i\in\mathcal{N}} \lambda_i x_i\T{x_i}\right)=
-\log\det\left(\mathbb{E}_{S\sim P_\mathcal{N}^\lambda}\bigg[I_d + \sum_{i\in S} x_i\T{x_i} \bigg]\right), \qquad \lambda\in[0,1]^n.
+    \begin{split}
+        L(\lambda) &\defeq
+\log\det\left(I_d + \sum_{i\in\mathcal{N}} \lambda_i x_i\T{x_i}\right)\\
+&= \log\det\left(\mathbb{E}_{S\sim P_\mathcal{N}^\lambda}\bigg[I_d + \sum_{i\in S} x_i\T{x_i} \bigg]\right),
+\quad \lambda\in[0,1]^n.
+\end{split}
 \end{equation}
 Note that $L$ also extends $V$, and follows naturally from the multi-linear extension by swapping the
 expectation and $\log \det$ in \eqref{eq:multi-linear}. Crucially, it is \emph{strictly concave} on $[0,1]^n$, a fact that  we exploit in the next section to maximize $L$ subject to the budget constraint in polynomial time.
-- 
cgit v1.2.3-70-g09d2

