Previous approaches towards designing truthful, budget feasible mechanisms for \textsc{Knapsack}~\cite{chen} and \textsc{Coverage}~\cite{singer-influence} build upon polynomial-time algorithms that compute an approximation of $OPT$, the optimal value in the full information case. Crucially, to be used in designing a truthful mechanism, such algorithms need also to be \emph{monotone}, in the sense that decreasing any of the costs $c_i$ leads to an increase of the estimate of $OPT$. In the cases of \textsc{Knapsack} and~\textsc{Coverage}, as well as in the case of \EDP{},  monotonicity  prevents using traditional approximation algorithms.

We address this issue by designing a convex relaxation of \EDP{}, and showing that it well approximates $OPT$. 

As noted above, \EDP{} is NP-hard. Designing a mechanism for this problem, as
we will see in Section~\ref{sec:mechanism}, will involve being able to find an approximation of its optimal value
$OPT$ defined in \eqref{eq:non-strategic}. In addition to being computable in
polynomial time and having a bounded approximation ratio to $OPT$, we will also
require this approximation to be non-increasing in the following sense:

\begin{definition}
Let $f$ be a function from $\reals^n$ to $\reals$. We say that $f$ is
\emph{non-decreasing (resp. non-increasing) along the $i$-th coordinate} iff:
\begin{displaymath}
\forall x\in\reals^n,\;
t\mapsto f(x+ te_i)\; \text{is non-decreasing (resp. non-increasing)}
\end{displaymath}
where $e_i$ is the $i$-th canonical basis vector of $\reals^n$. 

We say that $f$ is \emph{non-decreasing} (resp. \emph{non-increasing}) iff it
is non-decreasing (resp. non-increasing) along all coordinates.
\end{definition}


Such an approximation will be obtained by introducing a concave optimization
problem with a constant approximation ratio to \EDP{}
(Proposition~\ref{prop:relaxation}) and then showing how to approximately solve
this problem in a monotone way.

\subsection{A Concave Relaxation of \EDP}\label{sec:concave}

A classical way of relaxing combinatorial optimization problems is 
\emph{relaxing by expectation}, using the so-called \emph{multi-linear}
extension of the objective function $V$.

Let $P_\mathcal{N}^\lambda(S)$ be the probability of choosing the
set $S$ if we select each element $i$ in $\mathcal{N}$ independently with
probability $\lambda_i$:
\begin{displaymath}
    P_\mathcal{N}^\lambda(S) \defeq \prod_{i\in S} \lambda_i
    \prod_{i\in\mathcal{N}\setminus S}( 1 - \lambda_i).
\end{displaymath}
Then, the \emph{multi-linear} extension $F$ of $V$ is defined as the
expectation of $V$ under the probability distribution $P_\mathcal{N}^\lambda$:
\begin{equation}\label{eq:multi-linear}
    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)
\end{equation}

This function is an extension of $V$ in the following sense: $F(\id_S) = V(S)$ 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}. 

For the specific function $V$ defined in \eqref{modified}, the
multi-linear extension cannot be computed (and \emph{a fortiori} maximized) in
polynomial time. Hence, we introduce a new function $L$:
\begin{equation}\label{eq:our-relaxation}
\forall\,\lambda\in[0,1]^n,\quad L(\lambda) \defeq
\log\det\left(I_d + \sum_{i\in\mathcal{N}} \lambda_i x_i\T{x_i}\right),
\end{equation}
Note that this relaxation, 
follows naturally from the \emph{multi-linear} extension by swapping the
expectation and $V$ in \eqref{eq:multi-linear}:
\begin{displaymath}
    L(\lambda) = \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).
\end{displaymath}

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}}
    \left\{L(\lambda) \Big| \sum_{i=1}^{n} \lambda_i c_i
    \leq B\right\}
\end{equation}

The key property of our relaxation $L$ is that it has a bounded approximation
ratio to the multi-linear relaxation $F$. This is one of our main technical
contributions and is stated and proved in Lemma~\ref{lemma:relaxation-ratio}
found in Appendix. Moreover, the multi-linear relaxation $F$ has an exchange
property (see Lemma~\ref{lemma:rounding}) which allows us to relate its value to
$OPT$ through rounding. Together, these properties give the following
proposition which is also proved in the Appendix.

\begin{proposition}\label{prop:relaxation}
$L^*_c \leq 2 OPT + 2\max_{i\in\mathcal{N}}V(i)$.
\end{proposition}

\subsection{Solving a Convex Problem Monotonously}\label{sec:monotonicity}

Note, that the feasible set in Problem~\eqref{eq:primal} increases (for the
inclusion) when the cost decreases. As a consequence, $c\mapsto L^*_c$ is
non-increasing.

Furthermore, \eqref{eq:primal} being a convex optimization problem, using
standard convex optimization algorithms (Lemma~\ref{lemma:barrier} gives
a formal statement for our specific problem) we can compute
a $\varepsilon$-accurate approximation of its optimal value as defined below.

\begin{definition}
$a$ is an $\varepsilon$-accurate approximation of $b$ iff $|a-b|\leq \varepsilon$.
\end{definition}

Note however that an $\varepsilon$-accurate approximation of a non-increasing
function is not in general non-increasing itself. The goal of this section is
to approximate $L^*_c$ while preserving monotonicity. The estimator we
construct has a weaker form of monotonicity that we call
\emph{$\delta$-monotonicity}.

\begin{definition}
Let $f$ be a function from $\reals^n$ to $\reals$, we say that $f$ is
\emph{$\delta$-increasing along the $i$-th coordinate} iff:
\begin{equation}\label{eq:dd}
    \forall x\in\reals^n,\;
    \forall \mu\geq\delta,\;
    f(x+\mu e_i)\geq f(x)
\end{equation}
where $e_i$ is the $i$-th canonical basis vector of $\reals^n$. By extension,
$f$ is $\delta$-increasing iff it is $\delta$-increasing along all coordinates.

We define \emph{$\delta$-decreasing} functions by reversing the inequality in
\eqref{eq:dd}.
\end{definition}

We consider a perturbation of \eqref{eq:primal} by introducing:
\begin{equation}\tag{$P_{c, \alpha}$}\label{eq:perturbed-primal}
    L^*_c(\alpha) \defeq \max_{\lambda\in[\alpha, 1]^{n}}
    \left\{L(\lambda) \Big| \sum_{i=1}^{n} \lambda_i c_i
    \leq B\right\}
\end{equation}
Note that we have $L^*_c = L^*_c(0)$.  We will also assume that
$\alpha<\frac{1}{nB}$ so that \eqref{eq:perturbed-primal} has at least one
feasible point: $(\frac{1}{nB},\ldots,\frac{1}{nB})$. By computing
an approximation of $L^*_c(\alpha)$ as in Algorithm~\ref{alg:monotone}, we
obtain a $\delta$-decreasing approximation of $L^*_c$.

\begin{algorithm}[t]
    \caption{}\label{alg:monotone}
    \begin{algorithmic}[1]
        \State $\alpha \gets \varepsilon B^{-1}(\delta+n^2)^{-1} $ 

        \State Compute a $\frac{1}{2^{n+1}}\alpha\delta b$-accurate approximation of
        $L^*_c(\alpha)$
    \end{algorithmic}
\end{algorithm}

\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,
    $\varepsilon$-accurate approximation of $L^*_c$. The running time of the
    algorithm is $O\big(poly(n, d, \log\log\frac{1}{b\varepsilon\delta})\big)$
\end{proposition}


