From d010cdae55a7e19d78a24e46c1db3058d69347fc Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Wed, 15 Aug 2012 18:27:30 -0700
Subject: Repository restructuration (has to be clean now that it is shared ;)

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-\documentclass{article}
-\usepackage[utf8]{inputenc}
-\usepackage{amsmath,amsthm,amsfonts}
-\usepackage{comment}
-\newtheorem{lemma}{Lemma}
-\newtheorem{fact}{Fact}
-\newtheorem{example}{Example}
-\newtheorem{prop}{Proposition}
-\newcommand{\var}{\mathop{\mathrm{Var}}}
-\newcommand{\condexp}[2]{\mathop{\mathbb{E}}\left[#1|#2\right]}
-\newcommand{\expt}[1]{\mathop{\mathbb{E}}\left[#1\right]}
-\newcommand{\norm}[1]{\lVert#1\rVert}
-\newcommand{\tr}[1]{#1^*}
-\newcommand{\ip}[2]{\langle #1, #2 \rangle}
-\newcommand{\mse}{\mathop{\mathrm{MSE}}}
-\newcommand{\trace}{\mathop{\mathrm{tr}}}
-\begin{document}
-
-\section{Problem}
-
-\begin{itemize}
-\item  database $D = (X_i)_{1\leq i\leq n}$
-\item $(X_i)_{1\leq i\leq n}\in\big(\mathbf{R^d}\big)^n$ sampled in an i.i.d
-    fashion from the probability distribution $\mu$
-\end{itemize}
-
-We assume that there is a common variable $P$ which is the underlying
-phenomenon that we are interesting in, and all the variables in the database,
-are noisy realisations of this phenomenon:
-\begin{displaymath}
-    X_i = P + N_i
-\end{displaymath}
-where the $N_i$ are pairwise independent and also independent from $H$.
-
-
-We are interested in defining the $\emph{value}$ of a subset $S$ of the
-database $D$. The approaches to such a definition can be classified in two main
-families:
-\begin{itemize}
-    \item \emph{Information theoretic:} We use the entropy of $P$ as a measure
-        of the uncertainty. Then the value of a subset $S$ is the decrease of
-        entropy induced by revealing it:
-        \begin{displaymath}
-            V(S) = H(P) - H(P\,|\,S) 
-        \end{displaymath}
-    \item \emph{Statistical:} in this case you are trying to estimate the
-        phenomenon. The variance of the estimation is used as a measure of
-        the uncertainty. The value of a set $S$ can be defined as the average
-        reduction of the variance over all the points in the database once you
-        observe $S$:
-        \begin{displaymath}
-            V(S) = \frac{1}{|D|}\sum_{x\in D} \sigma_x^2 - \sigma_{x|A}^2
-        \end{displaymath}
-\end{itemize}
-
-Once you have defined a notion of value, there are several problems you can
-address:
-\begin{enumerate}
-    \item \emph{budget auctions:} the points in the database are associated
-        with users. Each user $i$ has a cost $c_i$ for revealing his data. You
-        are given a budget $B$ and you want to select a subset $S$ of users and
-        a payment scheme which is truthful. The value of the subset
-        $S$ should be as big as possible with the constraint that the payments
-        should stay within budget.
-    \item \emph{value sharing:} the goal is to find a value sharing method to
-        split the whole value of a subset $S$ among its participants.
-    \item \emph{k-best auctions:} this one is very similar to \emph{budget
-        auctions}, but instead of having a budget constraint, the constraint is
-        on the number of people in the subset $S$.
-\end{enumerate}
-
-These problems have already been studied, and optimal or near-optimal solution
-are already known when the value function is submodular. Fortunately, it can be
-shown that the information theoretic value defined above is submodular.
-
-\section{Submodularity}
-
-In this section, we will consider that we are given a \emph{universe} set $U$.
-A set function $f$ is a function defined on the power set of $U$, $\mathfrak{P}(U)$.
-
-A set function $f$ defined on $\mathfrak{P}(U)$ will be said \emph{increasing} if
-it is increasing with regards to inclusion, that is:
-
-\begin{displaymath}
-  \forall\,S\subseteq T,\quad f(S)\leq f(T)
-\end{displaymath}
-
-A \emph{decreasing} function on $\mathfrak{P}(U)$ is defined similarly.
-
-A set function $f$ defined on $\mathfrak{P}(U)$ is said to be
-\emph{submodular} if it verifies the diminishing returns property, that is,
-the marginal increments when adding a point to a set, is a set decreasing
-function. More formally, for any point $x$ in $U$, we can define the marginal
-increment of $f$ regarding $x$, it is the set function defined as:
-\begin{displaymath}
-    \Delta_x f(S) = f(S\cup\{x\}) - f(S)
-\end{displaymath}
-Then, $f$ is \emph{submodular} iff. for all $x$ in $U$, $\Delta_x f$ is a set
-decreasing function.
-
-Similarly, a \emph{supermodular} is a function whose marginal increments are set
-increasing functions.
-\begin{prop}
-  Let $R:\mathbf{R}\rightarrow \mathbf{R}$ be a decreasing concave function and
-  $f:\mathfrak{P}(U)\rightarrow\mathbf{R}$ be a decreasing submodular function,
-  then the composed function $R\circ f$ is increasing and supermodular.
-\end{prop}
-
-\begin{proof}
-  The increasingness of $R\circ f$ follows immediately from the decreasingness
-  of $R$ and $f$.
-  
-  For the supermodularity, let $S$ and $T$ be two sets such that $S\subseteq
-  T$. By decreasingness of $f$, we have:
-  \begin{displaymath}
-    \forall\,V,\quad f(T)\leq f(S)\quad\mathrm{and}\quad f(T\cup V)\leq f(S\cup V)
-  \end{displaymath}
-  
-  Thus, by concavity of $R$:
-  \begin{displaymath}\label{eq:base}
-    \forall\,V,\quad\frac{R\big(f(S)\big)-R\big(f(S\cup V)\big)}{f(S)-f(S\cup V)}
-    \leq\frac{R\big(f(T)\big)-R\big(f(T\cup V)\big)}{f(T)-f(T\cup V)}
-  \end{displaymath}
-  
-  $f$ is decreasing, so multiplying this last inequality by
-  $f(S)-f(S\cup V)$ and $f(T)-f(T\cup V)$ yields:
-  \begin{multline}
-    \forall V,\quad\Big(R\big(f(S)\big)-R\big(f(S\cup V)\big)\Big)\big(f(T)-f(T\cup V)\big)\\
-    \leq \Big(R\big(f(T)\big)-R\big(f(T\cup V)\big)\Big)\big(f(S)-f(S\cup V)\big)
-  \end{multline}
-
-  $f$ is submodular, so:
-  \begin{displaymath}
-    f(T\cup V)-f(T)\leq f(S\cup V) - f(S)
-  \end{displaymath}
-  
-  $R\circ f$ is increasing, so:
-  \begin{displaymath}
-    R\big(f(S)\big)-R\big(f(S\cup V)\big)\leq 0
-  \end{displaymath}
-
-  By combining the two previous inequalities, we get:
-    \begin{multline*}
-    \forall V,\quad\Big(R\big(f(S)\big)-R\big(f(S\cup V)\big)\Big)\big(f(S)-f(S\cup V)\big)\\
-    \leq \Big(R\big(f(S)\big)-R\big(f(S\cup V)\big)\Big)\big(f(T)-f(T\cup V)\big)
-  \end{multline*}
-
-  Injecting this last inequality into \eqref{eq:base} gives:
-  \begin{multline*}
-    \forall V,\quad\Big(R\big(f(S)\big)-R\big(f(S\cup V)\big)\Big)\big(f(S)-f(S\cup V)\big)\\
-    \leq \Big(R\big(f(T)\big)-R\big(f(T\cup V)\big)\Big)\big(f(S)-f(S\cup V)\big)
-  \end{multline*}
-
-  Dividing left and right by $f(S)-f(S\cup V)$ yields:
-  \begin{displaymath}
-    \forall V,\quad R\big(f(S)\big)-R\big(f(S\cup V)\big)
-    \leq R\big(f(T)\big)-R\big(f(T\cup V)\big)
-  \end{displaymath}
-  which is exactly the supermodularity of $R\circ f$.
-\end{proof}
-
-\end{document}
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