1 files changed, 0 insertions, 299 deletions
diff --git a/paper/sections/combinatorial.tex b/paper/sections/combinatorial.tex
deleted file mode 100644
index b744564..0000000
--- a/paper/sections/combinatorial.tex
+++ /dev/null
@@ -1,299 +0,0 @@
-\subsection{Submodularity of the problem}
-Let $\mathcal{U} = \{(v_u,c_u);\, u\in\neigh{X}\}$ with $v_u=p_uw_u$ and
-$c_u=p_u$.  Let $b\in\reals^+$ and $A\subseteq\mathcal{U}$, we denote by
-$\mathcal{O}(A,b)$ the optimal solution to:
-\begin{displaymath}
-    \begin{split}
-        &\max_{q\in[0,1]^n}\sum_{u\in\neigh{X}}q_uv_u\\
-        &\text{s.t. }\sum_{u\in\neigh{X}}q_uc_u\leq b\text{ and } q_u=0\text{ if
-    }u\notin A
-\end{split}
-\end{displaymath}
-so that the optimization problem \eqref{eq:relaxed-multi} for relaxed
-non-adaptive policies can be written:
-\begin{equation}\label{eq:sub}
-    \begin{split}
-        &\max_{S\subseteq X}\mathcal{O}\big(\neigh{S},k-|S|\big)\\
-        &\text{s.t. }|S|\leq k
-    \end{split}
-\end{equation}
-
-In this section, we will show how \eqref{eq:sub} can be reduced to a submodular
-maximization problem, leading to a fast combinatorial algorithm for the
-adaptive seeding model in the voter model.
-
-\begin{lemma}\label{lemma:nd}
-    Let $A\subseteq\mathcal{U}$ and $x=(v,c)\in\mathcal{U}\setminus A$, then
-    the function $b\mapsto \mathcal{O}(A\cup\{x\},b)-\mathcal{O}(A,b)$ is
-    non-decreasing over $\reals^+$.
-\end{lemma}
-
-\begin{proof}
-\emph{W.l.o.g} we can rename and order the pairs in $A$ so that:
-\begin{displaymath}
-\frac{v_1}{c_1}\geq \frac{v_2}{c_2}\geq\ldots\geq\frac{v_m}{c_m}.
-\end{displaymath}
-Then, $\mathcal{O}(A,b)$ has the following simple piecewise linear expression:
-\begin{equation}\label{eq:pw}
-    \mathcal{O}(A,b) = 
-    \begin{cases}
-        b\frac{v_1}{c_1}&\text{if }0\leq b<c_1\\
-        \sum_{k=1}^{i-1}v_k + \left(b-\sum_{k=1}^{i-1}c_k\right)\frac{v_i}{c_i}
-        &\text{if } \sum_{k=1}^{i-1}c_k\leq b< \sum_{k=1}^ic_k\\
-        \sum_{k=1}^m v_k&\text{if } b\geq\sum_{i=1}^m c_k
-
-    \end{cases}
-\end{equation}
-
-Let us define for $t\in\reals^+$, $n(t)\defeq \inf\Big\{i\text{ s.t.
-} \sum_{k=1}^i c_k > t\Big \}$ with $n(t)=+\infty$ when the set is empty. In
-particular, note that $x\mapsto n(t)$ is non-decreasing. Denoting
-$\partial_+\mathcal{O}_A$ the right derivative of $\mathcal{O}(A,\cdot)$, one
-can write $\partial_+\mathcal{O}_A(t)
-=\frac{v_{n(t)}}{c_{n(t)}}$, with the convention that
-$\frac{v_\infty}{c_\infty} = 0$.
-
-Writing $i \defeq \sup\Big\{j\text{ s.t.
-} \frac{v_j}{c_j}\geq\frac{v}{c}\Big\}$, it is easy to see that
-$\partial_+\mathcal{O}_{A\cup\{x\}}\geq\partial_+\mathcal{O}_A$. Indeed:
-\begin{enumerate}
-    \item if $n(t)\leq i$ then $\partial_+\mathcal{O}_{A\cup\{x\}}(t)
-        = \partial_+\mathcal{O}_A(t)= \frac{v_{n(t)}}{c_{n(t)}}$.
-    \item if $n(t)\geq i+1$ and $n(t-c)\leq i$ then $\partial_+\mathcal{O}_{A\cup\{x\}}(t)
-        = \frac{v}{c}\geq\frac{v_{n(t)}}{c_{n(t)}}= \partial_+\mathcal{O}_A(t)$.
-    \item if $n(t-c)\geq i+1$, then $\partial_+\mathcal{O}_{A\cup\{x\}}
-    = \frac{v_{n(t-c)}}{c_{n(t-c)}}\geq
-    \frac{v_{n(t)}}{c_{n(t)}}=\partial_+\mathcal{O}_A(t)$.
-\end{enumerate}
-
-Let us now consider $b$ and $c$ such that $b\leq c$. Then, using the integral
-representation of $\mathcal{O}(A\cup\{x\},\cdot)$ and $\mathcal{O}(A,\cdot)$, we get:
-\begin{multline*}
-    \mathcal{O}(A\cup\{x\},c)-\mathcal{O}(A\cup\{x\},b)=\int_b^c\partial_+\mathcal{O}_{A\cup\{x\}}(t)dt\\
-    \geq\int_b^c\partial_+\mathcal{O}_A(t)dt=\mathcal{O}(A,c)-\mathcal{O}(A,b)
-\end{multline*}
-Re-ordering the terms, $\mathcal{O}(A\cup\{x\},c)-\mathcal{O}(A,c)\geq
-\mathcal{O}(A\cup\{x\},b)-\mathcal{O}(A,b)$
-which concludes the proof of the lemma.
-\end{proof}
-
-\begin{lemma}\label{lemma:sub}
-    Let $b\in\reals^+$, then the function $A\mapsto \mathcal{O}(A,b)$ is submodular.
-\end{lemma}
-
-\begin{proof}
-    Let $A\subseteq\mathcal{U}$ and $x, y\in\mathcal{U}\setminus A$. Using the
-    second-order characterization of submodular functions, it suffices to show
-    that:
-    \begin{equation}\label{eq:so}
-        \mathcal{O}(A\cup\{x\},b)-\mathcal{O}(A,b)\geq
-        \mathcal{O}(A\cup\{y\}\cup\{x\},b)-\mathcal{O}(A\cup\{y\},b)
-    \end{equation}
-
-    Let us write $x=(v_x, c_x)$ and $y=(v_y,c_y)$. We distinguish two cases
-    based on the relative position of $\frac{v_x}{c_x}$ and $\frac{v_y}{c_y}$.
-    The following notations will be useful: $S_A^x \defeq \big\{(v,c)\in
-    A\text{ s.t.  }\frac{v_x}{c_x}\leq\frac{v}{c}\big\}$ and $P_A^x\defeq
-    A\setminus S_A^x$.
-
-    \textbf{Case 1:} If $\frac{v_y}{c_y}\geq \frac{v_x}{c_x}$, then one can
-    write:
-    \begin{gather*}
-        \mathcal{O}(A\cup\{y\}\cup\{x\},b) = \mathcal{O}(P_A^y\cup\{y\},b_1)+
-        \mathcal{O}(S_A^y\cup\{x\},b_2)\\
-        \mathcal{O}(A\cup\{y\},b) = \mathcal{O}(P_A^y\cup\{y\},b_1)
-        + \mathcal{O}(S_A^y,b_2)
-    \end{gather*}
-    where $b_1$ is the fraction of the budget $b$ spent on $P_A^y\cup\{y\}$ and
-    $b_2=b-b_1$.
-    
-    Similarly:
-    \begin{gather*}
-        \mathcal{O}(A\cup\{x\},b) = \mathcal{O}(P_A^y, c_1) + \mathcal{O}(S_A^y\cup\{x\},c_2)\\
-        \mathcal{O}(A, b) = \mathcal{O}(P_A^y, c_1) + \mathcal{O}(S_A^y,c_2)
-    \end{gather*}
-    where $c_1$ is the fraction of the budget $b$ spent on $P_A^y$ and $c_2
-    = b - c_1$. 
-
-    Note that $b_1\geq c_1$: an optimal solution will first spent as much
-    budget as possible on $P_A^y\cup\{y\}$ before adding elements in
-    $S_A^y\cup\{x\}$. We can now rewrite \eqref{eq:so}:
-    \begin{displaymath}
-        \mathcal{O}(S_A^y\cup\{x\},c_2)+\mathcal{O}(S_A^y,c_2)\geq
-        \mathcal{O}(S_A^y\cup\{x\},b_2)+\mathcal{O}(S_A^y,b_2)
-    \end{displaymath}
-    with $c_2\geq b_2$. This inequality is true by Lemma~\ref{lemma:nd}.
-
-    \textbf{Case 2:} If $\frac{v_x}{c_x}\geq \frac{v_y}{c_y}$, we now decompose
-    the solution on $P_A^x$ and $S_A^x$:
-    \begin{gather*}
-        \mathcal{O}(A\cup\{x\},b) = \mathcal{O}(P_A^x\cup\{x\},b_1)
-        + \mathcal{O}(S_A^x,b_2)\\
-        \mathcal{O}(A,b) = \mathcal{O}(P_A^x,c_1)+\mathcal{O}(S_A^x,c_2)
-        \intertext{and}
-        \mathcal{O}(A\cup\{y\}\cup\{x\},b) = \mathcal{O}(P_A^x\cup\{x\},b_1)
-        + \mathcal{O}(S_A^x\cup\{y\},b_2)\\
-        \mathcal{O}(A\cup\{y\},b) = \mathcal{O}(P_A^x,c_1)+\mathcal{O}(S_A^x\cup\{y\},c_2)
-    \end{gather*}
-    with $b_1+b_2=b$, $c_1+c_2=b$ and $b_2\leq c_2$. The equation \eqref{eq:so}
-    can be rewritten:
-    \begin{displaymath}
-        \mathcal{O}(S_A^x,b_2)-\mathcal{O}(S_A^x,c_2)\geq
-        \mathcal{O}(S_A^x\cup\{u\},b_2)-\mathcal{O}(S_A^x\cup\{y\},c_2)
-    \end{displaymath}
-    Reordering the terms, we get:
-    \begin{displaymath}
-        \mathcal{O}(S_A^x\cup\{y\},c_2) -\mathcal{O}(S_A^x,c_2)\geq
-        \mathcal{O}(S_A^x\cup\{u\},b_2)-\mathcal{O}(S_A^x,b_2)
-    \end{displaymath}
-    with $c_2\geq b_2$. This is again true by Lemma~\ref{lemma:nd}.
-\end{proof}
-
-\begin{proposition}\label{prop:sub}
-    Let $b\in\mathbf{R}^+$, then the function
-    $S\mapsto\mathcal{O}(\neigh{S},b)$ is submodular over $2^X$.
-\end{proposition}
-
-\begin{proof}
-    Let us consider $S$ and $T$ such that $S\subseteq T\subseteq X$ and $x\in
-    X\setminus T$. In particular, note that $\neigh{S}\subseteq\neigh{T}$. 
-
-    Let us write $\neigh{S\cup\{x\}}=\neigh{S}\cup R$ with $\neigh{S}\cap
-    R=\emptyset$ and similarly, $\neigh{T\cup\{x\}}=\neigh{T}\cup R'$ with
-    $\neigh{T}\cap R'=\emptyset$. Using these notations, it is clear that
-    $R'\subseteq R$. Let us enumerate the elements in $R'$ using an arbitrary
-    order: $R'=\{u_1,\ldots,u_k\}$. Then one can write:
-    \begin{displaymath}
-        \begin{split}
-            \mathcal{O}(\neigh{T\cup\{x\}},b)&=\mathcal{O}(\neigh{T}\cup R',b)\\
-            &=\sum_{i=1}^k\mathcal{O}(\neigh{T}\cup\{u_1,\ldots u_i\},b)
-            -\mathcal{O}(\neigh{T}\cup\{u_1,\ldots u_{i-1}\},b)\\
-            &\leq \sum_{i=1}^k\mathcal{O}(\neigh{S}\cup\{u_1,\ldots u_i\},b)
-            -\mathcal{O}(\neigh{S}\cup\{u_1,\ldots u_{i-1}\},b)\\
-            &=\mathcal{O}(\neigh{S}\cup R',b)-\mathcal{O}(\neigh{S},b)
-        \end{split}
-    \end{displaymath}
-    where the inequality comes from the submodularity of
-    $A\mapsto\mathcal{O}(A,b)$ proved in Lemma~\ref{lemma:sub}. This same
-    function is also obviously set-increasing, hence:
-    \begin{displaymath}
-        \begin{split}
-            \mathcal{O}(\neigh{S}\cup R',b)-\mathcal{O}(\neigh{S},b)
-            &\leq \mathcal{O}(\neigh{S}\cup R,b)-\mathcal{O}(\neigh{S},b)\\
-            &=\mathcal{O}(\neigh{S\cup\{x\}},b)-\mathcal{O}(\neigh{S},b)
-        \end{split}
-    \end{displaymath}
-    This concludes the proof of the proposition.
-\end{proof}
-
-Using Proposition~\ref{prop:sub}, we can finally reduce \eqref{eq:sub} to
-a submodular optimization problem by treating $|S|$ as a variable to optimize.
-Specifically, \eqref{eq:sub} is equivalent to the following problem:
-\begin{equation}\label{eq:sub2}
-\begin{split}
-    \max_{1\leq t\leq k}\max_{|S|\leq k-t}\mathcal{O}(\neigh{S},t)
-\end{split}
-\end{equation}
-
-\begin{algorithm}
-    \caption{Combinatorial algorithm}
-    \label{alg:comb}
-    \algsetup{indent=2em}
-    \begin{algorithmic}[1]
-        \STATE $S\leftarrow \emptyset$
-        \FOR{$t=1$ \TO $k-1$}
-            \STATE $S_t\leftarrow \emptyset$
-            \FOR{$i=1$ \TO $k-t$}
-                \STATE $x^*\leftarrow\argmax_{x\in
-                X\setminus S_t}\mathcal{O}(\neigh{S_t\cup\{x\}},t)
-                -\mathcal{O}(\neigh{S_t},t)$\label{line:argmax}
-                \STATE $S_t\leftarrow S_t\cup\{x^*\}$
-            \ENDFOR
-            \IF{$\mathcal{O}(\neigh{S_t},t)>\mathcal{O}(\neigh{S},k-|S|)$}
-                \STATE $S\leftarrow S_t$
-            \ENDIF
-        \ENDFOR
-        \RETURN $S$
-    \end{algorithmic}
-\end{algorithm}
-
-\begin{proposition}
-    Let $S$ be the set computed by Algorithm~\ref{alg:comb} and let us denote
-    by $A(S)$ the value of the adaptive policy which selects $S$ on the first
-    day. Then:
-    \begin{displaymath}
-        A(S) \geq \frac{1}{2}\left(1-\frac{1}{e}\right)OPT.
-    \end{displaymath}
-    Furthermore, Algorithm~\ref{alg:comb} runs in time $O\left(n\log
-    n+k^2m\min(\frac{k}{p_\text{min}},n)\right)$, where $m=|X|$, $n=|\neigh{X}|$
-    and $p_\text{min} =\min\{p_u,u\in\neigh{X}\}$.
-\end{proposition}
-
-\begin{proof}
-    For each realization of the neighbors $R$, let us define by $X_R$ the
-    random set which includes each $u\in R$ with probability $q_u$ on the
-    second day and:
-    \begin{displaymath}
-        \tilde{X}_R=
-    \begin{cases}
-        X_R&\text{if } |X_R|\leq t\\
-        \emptyset&\text{otherwise}
-    \end{cases}
-    \end{displaymath}
-    where $t = k-|S|$. $\tilde{X}_R$ is a random variable over the feasible
-    solutions on the second day. As a consequence:
-    \begin{displaymath}
-        A(S) \geq \sum_{R\subseteq\neigh{S}} p_R\mathbb{E}\big[f(\tilde{X}_R)\big]
-    \end{displaymath}
-    Let us define by $Y$ the random set which includes each $u\in\neigh{X}$
-    with probability $p_uq_u$ and:
-    \begin{displaymath}
-        \tilde{Y}=
-        \begin{cases}
-            Y&\text{if } |Y|\leq t\\
-            \emptyset&\text{otherwise}
-        \end{cases}.
-    \end{displaymath}
-    It is easy to see that $\tilde{X}_R$ is the conditional expectation of
-    $\tilde{Y}$ given
-    $R$. Hence:
-    \begin{displaymath}
-        \sum_{R\subseteq\neigh{S}} p_R\mathbb{E}\big[f(\tilde{X}_R)\big]
-        = \mathbb{E}\big[f(\tilde{Y})\big]
-    \end{displaymath}
-    But:
-    \begin{align*}
-        \mathbb{E}\big[f(\tilde{Y})\big]
-        &=\sum_{T\subseteq\neigh{X}}\mathbb{P}\big[\tilde{Y}=T\big]f(T)
-    = \sum_{\substack{T\subseteq\neigh{X}\\|T|\leq
-    t}}\mathbb{P}\big[Y=T\big]f(T)\\
-    &= \mathbb{E}\big[f(Y)\big]-\sum_{\substack{T\subseteq\neigh{X}\\|T|>
-    t}}\mathbb{P}\big[Y=T\big]f(T)
-    \end{align*}
-    Roughly:
-    \begin{displaymath}
-        \sum_{\substack{T\subseteq\neigh{X}\\|T|>
-        t}}\mathbb{P}\big[Y=T\big]f(T)\leq
-        \frac{1}{2}\mathbb{E}\big[f(Y)\big]
-    \end{displaymath}
-    Combining the above inequalities we get:
-    \begin{displaymath}
-        A(S)\geq \frac{1}{2} \mathbb{E}\big[f(Y)\big]
-    \end{displaymath}
-    But $\mathbb{E}\big[f(Y)\big]$ is precisely the value of the non-adaptive
-    solution computed by Algorithm~\ref{alg:comb} which is
-    a $\left(1-\frac{1}{e}\right)$ approximation of the
-    optimal non adaptive solution. Hence:
-    \begin{displaymath}
-        A(S) \geq \frac{1}{2}\left(1-\frac{1}{e}\right) OPT_{NA}
-    \end{displaymath}
-    Finally, we conclude with Proposition~\ref{prop:gap}
-\end{proof}
-
-\subsection{From expectation to high probability}
-
-\subsection{Questions}
-
-\begin{itemize}
-    \item Can it be parallelized (map-reduce)?
-\end{itemize}