From d9c6b8d8e3a3c5ae02e80a9f8db45e845a1a5235 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Sat, 3 Nov 2012 16:48:32 +0100
Subject: More fixes on section 3

---
 main.tex | 107 +++++++++++++++++++++++++++++++++++++++++----------------------
 1 file changed, 70 insertions(+), 37 deletions(-)

(limited to 'main.tex')

diff --git a/main.tex b/main.tex
index 178ad1f..56fba56 100644
--- a/main.tex
+++ b/main.tex
@@ -73,15 +73,23 @@ and to $S_G$ otherwise yields a 8.34 approximation mechanism. For specific
 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 $\mathcal{N}$.
-
-We say that $R_\mathcal{N}$ is a fractional relaxation of $V$ over the set
-$\mathcal{N}$, if (a) $R_\mathcal{N}$ is a function defined on the hypercube
-$[0, 1]^{|\mathcal{N}|}$ and (b) for all $S\subseteq\mathcal{N}$, if
+denotes a fractional relaxation of the function $V$ over the set
+$\mathcal{N}$. We say that $R_\mathcal{N}$ is a fractional relaxation of $V$
+over the set $\mathcal{N}$, if (a) $R_\mathcal{N}$ is a function defined on the
+hypercube $[0, 1]^{|\mathcal{N}|}$ and (b) for all $S\subseteq\mathcal{N}$, if
 $\mathbf{1}_S$ denotes the indicator vector of $S$ we have
-$R_\mathcal{N}(\mathbf{1}_S) = V(S)$. A relaxation which is commonly used, due
-to its well-behaved properties in the context of maximizing submodular
-functions, is the \emph{multi-linear} extension:
+$R_\mathcal{N}(\mathbf{1}_S) = V(S)$. The optimization program
+\eqref{eq:non-strategic} extends naturally to relaxations:
+\begin{displaymath}
+    OPT(R_\mathcal{N}, B) = \argmax_{\lambda\in[0, 1]^{|\mathcal{N}|}}
+    \left\{R_\mathcal{N}(\lambda) \mid \sum_{i=1}^{|\mathcal{N}|} \lambda_i c_i
+    \leq B\right\}
+\end{displaymath}
+
+
+A relaxation which is commonly used, due to its well-behaved properties in the
+context of maximizing submodular functions, is the \emph{multi-linear}
+extension:
 \begin{equation}\label{eq:multilinear}
     F_\mathcal{N}^V(\lambda) 
     = \mathbb{E}_{S\sim P_\mathcal{N}^\lambda}\left[V(S)\right]
@@ -165,21 +173,28 @@ We can now state the main result of this section:
 
 \subsection{Proof of theorem~\ref{thm:main}}
 
-\stratis{Explain what are the main steps in the proof}
+The proof of the properties of the mechanism is broken down into lemmas.
+Monotonicity and budget feasibility follows from the analysis of Chen et al.
+\cite{chen}. The proof of the approximation ratio is done in
+lemma~\ref{lemma:approx} and uses a bound on our concave relaxation
+$L_\mathcal{N}$ (lemma~\ref{lemma:relaxation}) which is our main technical
+contribution. The proof of this lemma is done in a dedicated section
+(\ref{sec:relaxation}).
+
 \begin{lemma}\label{lemma:monotone}
 The mechanism is monotone.
 \end{lemma}
 
 \begin{proof}
-    Suppose, for contradiction, that there exists a user $i$ that has been
-    selected by the mechanism and that would not be selected had he reported
-    a cost $c_i'\leq c_i$ (all the other costs staying the same).
+    Suppose, for contradiction, that there exists an agent $i$ that has been
+    selected by the mechanism and that would not have been selected had she
+    reported a cost $c_i'\leq c_i$ (all the other costs staying the same).
 
     If $i\neq i^*$ and $i$ has been selected, then we are in the case where
-    $L_\mathcal{N}(x^*) \geq C V(i^*)$ and $i$ was included in the result set by the greedy
-    part of the mechanism. By reporting a cost $c_i'\leq c_i$, using the
-    submodularity of $V$, we see that $i$ will satisfy the greedy selection
-    rule:
+    $L_\mathcal{N}(x^*) \geq C V(i^*)$ and $i$ was included in the result set
+    by the greedy part of the mechanism. By reporting a cost $c_i'\leq c_i$,
+    using the submodularity of $V$, we see that $i$ will satisfy the greedy
+    selection rule:
     \begin{displaymath}
         i = \argmax_{j\in\mathcal{N}\setminus S} \frac{V(S\cup\{j\})
         - V(S)}{c_j}
@@ -194,10 +209,11 @@ The mechanism is monotone.
     \end{align*}
     Hence $i$ will still be included in the result set.
 
-    If $i = i^*$, $i$ is included iff $L_\mathcal{N}(x^*) \leq C V(i^*)$. Reporting $c_i'$
-    instead of $c_i$ does not change the value $V(i^*)$ nor $L_\mathcal{N}(x^*)$ (which is
-    computed over $\mathcal{N}\setminus\{i^*\}$). Thus $i$ is still included by
-    reporting a different cost.
+    If $i = i^*$, $i$ is included iff $L_\mathcal{N}(x^*) \leq C V(i^*)$.
+    Reporting $c_i'$ instead of $c_i$ does not change the value $V(i^*)$ nor
+    $L_\mathcal{N}(x^*)$ (which is computed over
+    $\mathcal{N}\setminus\{i^*\}$). Thus $i$ is still included by reporting
+    a different cost.
 \end{proof}
 
 \begin{lemma}\label{lemma:budget-feasibility}
@@ -208,14 +224,13 @@ The mechanism is budget feasible.
 Let us denote by $S_G$ the set selected by the greedy heuristic in the
 mechanism of Algorithm~\ref{mechanism}. Let $i\in S_G$, let us also denote by
 $S_i$ the solution set that was selected by the greedy heuristic before $i$ was
-added. Recall from \cite{chen} that the following holds for any submodular
-function: if the point $i$ was selected by the greedy heuristic, then:
+added. We use the following result from Chen et al. \cite{chen}, which bounds
+the reported cost of an agent selected by the greedy heuristic, and holds for
+any submodular function $V$:
 \begin{equation}\label{eq:budget}
     c_i \leq \frac{V(S_i\cup\{i\}) - V(S)}{V(S_G)} B
 \end{equation}
 
-\stratis{Use ``recall'' only for  something the reader is likely to know. }
-
 Assume now that our mechanism selects point $i^*$. In this case, his payment
 his $B$ and the mechanism is budget-feasible.
 
@@ -227,7 +242,7 @@ shows that the threshold payment of user $i$ is bounded by:
 
 Hence, the total payment is bounded by:
 \begin{displaymath}
-    \sum_{i\in S_M} \frac{V(S_i\cup\{i\}) - V(S_i)}{V(S_G)} B \leq B
+    \sum_{i\in S_M} \frac{V(S_i\cup\{i\}) - V(S_i)}{V(S_G)} B \leq B\qed
 \end{displaymath}
 \end{proof}
 
@@ -299,19 +314,37 @@ gives the result of the theorem.
 
 \subsection{An approximation ratio for $L_\mathcal{N}$}\label{sec:relaxation}
 
-Our main contribution is lemma~\ref{lemma:relaxation} which gives an
-approximation ratio for $L_\mathcal{N}$ and is key in the proof of
-theorem~\ref{thm:main}.
-
-It has been observed already (see for example \cite{dughmi}) that the
-multi-linear extension presents the cross-convexity property: it is convex along
-any direction $e_i-e_j$ where $e_i$ and $e_j$ are two elements of the standard
-basis. This property allows to trade between two fractional components of
-a point without diminishing the value of the relaxation. The following lemma
-follows is inspired by a similar lemma from \cite{dughmi} but also ensures that
-the points remain feasible during the trade. 
+Since $L_\mathcal{N}$ is a relaxation of the value function $V$, we have:
+\begin{displaymath}
+    OPT(V,\mathcal{N},B) \leq OPT(L_\mathcal{N}, B)
+\end{displaymath}
+However, for the purpose of proving theorem~\ref{thm:main}, we need to bound
+$L_\mathcal{N}$ from above (up to a multiplicative factor) by $V$. We use the
+\emph{pipage rounding} method from Ageev and Sviridenko \cite{pipage}, where
+$L_\mathcal{N}$ is first bounded from above by $F_\mathcal{N}$, which is itself
+subsequently bounded by $V$. While the latter part is general an can be apply
+to the multi-linear extension of any submodular function, the former part is
+specific to our choice for the function $L_\mathcal{N}$ and is our main
+technical contribution.
+
+First we prove a variant of the $\varepsilon$-convexity of the multi-linear
+extension \eqref{eq:multilinear} introduced by Ageev and Sviridenko
+\cite{pipage} which allows to trade a fractional component for another until
+one of them becomes integral, without loosing any value. This property is also
+referred to in the literature as \emph{cross-convexity} (see \emph{e.g.}
+\cite{dughmi}). Formally, this property states that if we define:
+\begin{displaymath}
+    \tilde{F}_\lambda(\varepsilon) \defeq F_\mathcal{N}\big(\lambda + \varepsilon(e_i
+    - e_j)\big)
+\end{displaymath}
+where $e_i$ and $e_j$ are two vectors of the standard basis of
+$\reals^{|\mathcal{N}|}$, then $\tilde{F}$ is convex. Hence its maximum over the interval:
+\begin{displaymath}
+ I_\lambda = \Big[\max(-\lambda_i,\lambda_j-1), \min(1-\lambda_i, \lambda_j)\Big]
+\end{displaymath}
+is attained at one of the boundaries of $I_\lambda$ for which one of the $i$-th
+or the $j$-th component of $\lambda$ becomes integral.
 
-\stratis{explain how the proof below relates to the $\epsilon$-convexity of sviridenko}
 \begin{lemma}[Rounding]\label{lemma:rounding}
     For any feasible $\lambda\in[0,1]^{|\mathcal{N}|}$, there exists a feasible
     $\bar{\lambda}\in[0,1]^{|\mathcal{N}|}$ such that at most one of its component is
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