From a53c28eebf8926d02a835dc55a903b6528c8e784 Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Thu, 4 Jul 2013 11:58:04 -0700
Subject: added lower bound in proof, some wording changed

---
 approximation.tex | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

(limited to 'approximation.tex')

diff --git a/approximation.tex b/approximation.tex
index cc861e8..de1fecd 100644
--- a/approximation.tex
+++ b/approximation.tex
@@ -46,17 +46,17 @@ 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].
+= \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.
 \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}. 
 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 $V$  that we study here, given by \eqref{modified}. Hence, we introduce a new extension $L:[0,1]^n\to\reals$:
+polynomial time for  the value function $V$  that we study here, given by \eqref{modified}. Hence, we introduce a new extension $L:[0,1]^n\to\reals$ s.t.~
 \begin{equation}\label{eq:our-relaxation}
-\forall\,\lambda\in[0,1]^n,\quad L(\lambda) \defeq
+\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).
+\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.
 \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.
@@ -91,7 +91,7 @@ In particular, for $L_c^*\defeq \max_{\lambda\in \dom_c} L(\lambda)$ the optimal
 \begin{proposition}\label{prop:relaxation}
 $OPT\leq L^*_c \leq 2 OPT + 2\max_{i\in\mathcal{N}}V(i)$.
 \end{proposition}
-The proof of this proposition can be found in Appendix~\ref{proofofproprelaxation}. As we discuss in the next section, $L^*_c$ can be computed a polynomial time algorithm at arbitrary accuracy; however, the outcome of this computation may not necessarily be monotone: we will address this by converting this poly-time estimator of $L^*_c$ to one that is ``almost'' monotone.%The optimization program \eqref{eq:non-strategic} extends naturally to such
+The proof of this proposition can be found in Appendix~\ref{proofofproprelaxation}. As we discuss in the next section, $L^*_c$ can be computed a polynomial time algorithm at arbitrary accuracy. However, the outcome of this computation may not necessarily be monotone; we address this by converting this poly-time estimator of $L^*_c$ to one that is ``almost'' monotone.%The optimization program \eqref{eq:non-strategic} extends naturally to such
 %a relaxation. We define:
 %\begin{equation}\tag{$P_c$}\label{eq:primal}
 %    L^*_c \defeq \max_{\lambda\in[0, 1]^{n}}
@@ -107,14 +107,14 @@ The proof of this proposition can be found in Appendix~\ref{proofofproprelaxatio
 %$OPT$ through rounding. Together, these properties give the following
 %proposition which is also proved in the Appendix.
 
-\subsection{Polynomial-Time, Almost-Monotonous Approximation}\label{sec:monotonicity}
- The $\log\det$ objective function of \eqref{eq:primal} is strictly concave and \emph{self-concordant} \cite{boyd2004convex}. The maximization of a concave, self-concordant function subject to a set of linear constraints can be performed through the \emph{barrier method} (see, \emph{e.g.}, \cite{boyd2004convex} Section 11.5.5 for  general self-concordant optimization as well as \cite{vandenberghe1998determinant} for a detailed treatise of the $\log\det$ objective). The performance of the barrier method is summarized in our case by the following lemma:
+\subsection{Polynomial-Time, Almost-Monotone Approximation}\label{sec:monotonicity}
+ The $\log\det$ objective function of \eqref{eq:primal} is strictly concave and \emph{self-concordant} \cite{boyd2004convex}. The maximization of a concave, self-concordant function subject to a set of linear constraints can be performed through the \emph{barrier method} (see, \emph{e.g.}, \cite{boyd2004convex} Section 11.5.5 for  general self-concordant optimization as well as \cite{vandenberghe1998determinant} for a detailed treatment of the $\log\det$ objective). The performance of the barrier method is summarized in our case by the following lemma:
 \begin{lemma}[\citeN{boyd2004convex}]\label{lemma:barrier}
 For any $\varepsilon>0$, the barrier method computes  an 
 approximation $\hat{L}^*_c$ that is $\varepsilon$-accurate, \emph{i.e.}, it satisfies $|\hat L^*_c- L^*_c|\leq \varepsilon$, in time $O\left(poly(n,d,\log\log\varepsilon^{-1})\right)$.
 \end{lemma}
 
- Clearly, the optimal value $L^*_c$ of \eqref{eq:primal} is monotone in $c$. Formally, for any two $c,c'\in \reals_+^n$ s.t.~$c\leq c'$ coordinate-wise, $\dom_{c'}\subseteq \dom_c$ and thus $L^*_c\geq L^*_{c'}$. Hence, the map $c\mapsto L^*_c$ is non-increasing. Unfortunately, the same is not true for the output $\hat{L}_c^*$ of the barrier method: there is no guarantee that the $\epsilon$-accurate approximation $\hat{L}^*_c$ will exhibit any kind of monotonicity.
+ Clearly, the optimal value $L^*_c$ of \eqref{eq:primal} is monotone in $c$. Formally, for any two $c,c'\in \reals_+^n$ s.t.~$c\leq c'$ coordinate-wise, $\dom_{c'}\subseteq \dom_c$ and thus $L^*_c\geq L^*_{c'}$. Hence, the map $c\mapsto L^*_c$ is non-increasing. Unfortunately, the same is not true for the output $\hat{L}_c^*$ of the barrier method: there is no guarantee that the $\epsilon$-accurate approximation $\hat{L}^*_c$ exhibits any kind of monotonicity.
 
 Nevertheless, it is possible to use the barrier method to construct an approximation of $L^*_{c}$ that is ``almost'' monotone. More specifically, given $\delta>0$,  we will say that $f:\reals^n\to\reals$ is
 \emph{$\delta$-decreasing}  if
@@ -125,7 +125,7 @@ Nevertheless, it is possible to use the barrier method to construct an approxima
 where $e_i$ is the $i$-th canonical basis vector of $\reals^n$.
 In other words, $f$ is $\delta$-decreasing if increasing any coordinate by $\delta$ or more at input $x$ ensures that the output will be at most $f(x)$.
 
-Our next technical result establishes that, using the barrier method, it is possible to construct an algorithm that computes $L^*_c$ at arbitrary accuracy in polynomial time \emph{and} is $\delta$-monotone. We achieve this by restricting the optimization over a subset of $\dom_c$ at which the concave relaxation $L$ is ``sufficiently'' concave. Formally, for $\alpha\geq 0$ let $$\textstyle\dom_{c,\alpha} \defeq \{\lambda \in [\alpha,1]^n: \sum_{i\in \mathcal{N}}c_i\lambda_i \leq B\}\subseteq  \dom_c . $$ 
+Our next technical result establishes that, using the barrier method, it is possible to construct an algorithm that computes $L^*_c$ at arbitrary accuracy in polynomial time \emph{and} is $\delta$-decreasing. We achieve this by restricting the optimization over a subset of $\dom_c$ at which the concave relaxation $L$ is ``sufficiently'' concave. Formally, for $\alpha\geq 0$ let $$\textstyle\dom_{c,\alpha} \defeq \{\lambda \in [\alpha,1]^n: \sum_{i\in \mathcal{N}}c_i\lambda_i \leq B\}\subseteq  \dom_c . $$ 
 Note that $\dom_c=\dom_{c,0}.$ Consider the following perturbation of the concave relaxation \eqref{eq:primal}:
 \begin{align}\tag{$P_{c,\alpha}$}\label{eq:perturbed-primal}
 \begin{split}  \text{Maximize:} &\qquad L(\lambda)\\
@@ -189,7 +189,7 @@ Note that $\dom_c=\dom_{c,0}.$ Consider the following perturbation of the concav
     \end{algorithmic}
 \end{algorithm}
 
-Our construction of a $\delta$-decreasing, $\varepsilon$-accurate approximator of $\hat{L}_c^*$ proceeds as follows: first, it computes an appropriately selected lower bound $\alpha$; using this bound, it proceeds by solving the perturbed problem \eqref{eq:perturbed-primal} using the barrier method, also at an appropriately selected accuracy $\varepsilon'$. The corresponding output is returned as an approximation of $L^*_c$. A summary of algorithm and the specific choices of $\alpha$ and $\varepsilon'$ are given in Algorithm~\ref{alg:monotone}. The following proposition, which is proved in Appendix~\ref{proofofpropmonotonicity}, establishes that this algorithm has the desirable properties:
+Our construction of a $\delta$-decreasing, $\varepsilon$-accurate approximator of $\hat{L}_c^*$ proceeds as follows: first, it computes an appropriately selected lower bound $\alpha$; using this bound, it proceeds by solving the perturbed problem \eqref{eq:perturbed-primal} using the barrier method, also at an appropriately selected accuracy $\varepsilon'$, obtaining thusly a $\varepsilon'$-accurate approximation of $L^*_{c,\alpha}\defeq \max_{\lambda\in \dom_{c,\alpha}} L(\lambda)$ . The corresponding output is returned as an approximation of $L^*_c$. A summary of algorithm and the specific choices of $\alpha$ and $\varepsilon'$ are given in Algorithm~\ref{alg:monotone}. The following proposition, which is proved in Appendix~\ref{proofofpropmonotonicity}, establishes that this algorithm has the desirable properties:
 \begin{proposition}\label{prop:monotonicity}
     For any $\delta\in(0,1]$ and any $\varepsilon\in(0,1]$,
     Algorithm~\ref{alg:monotone} computes a $\delta$-decreasing,
-- 
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