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@@ -147,19 +147,26 @@ $\hat{x}$ and whose revenue is a constant approximation to $\Rev(\hat{x},F)$?}
\paragraph{Single-item case.}
To make things more concrete, let us first look at the single-item case, which
-has already been studied extensively and is well-understood. In this setting, $\hat{x}$ is a real number and the
-function $\Rev(\hat{x},F)$ defined above is obtained by maximizing the revenue curve $R(\hat{x})$ subject to the allocation constraint, and turns out
-to be a very useful object to understand the revenue maximizing multiple-agent
-single-item auction (see \citep{hartline}, Chapter 3).
+has already been studied extensively and is well understood. In this setting,
+$\hat{x}$ is a real number and the function $\Rev(\hat{x},F)$ defined above is
+obtained by maximizing the revenue curve $R(x)$ subject to the allocation
+constraint $x\leq \hat{x}$, and turns out to be a very useful object to
+understand the revenue maximizing multiple-agent single-item auction (see
+\citep{hartline}, Chapter 3).
In particular, if the type of the agent (her value) is drawn from a regular
-distribution, the optimal mechanism which serves the agent with ex-ante
-allocation probability $\hat{x}$ has revenue $\Rev(\hat{x},F)$, given by solving \begin{align*}
-\max_{p} p(1-F(p)) \\
-\text{subject to }& 1 - F(p) \leq \hat{x}
+distribution, the revenue curve $R(x)$ is equal to the posted price revenue
+curve $P(x) = xF^{-1}(1-x)$ and the optimal mechanism which serves the agent
+with ex-ante allocation probability at most $\hat{x}$ has revenue $\Rev(\hat{x},F)$,
+given by solving
+\begin{align*}
+ \begin{split}
+ \max_{x} & \;xF^{-1}(1-x) \\
+\text{subject to }& \; x \leq \hat{x}
+ \end{split}
\end{align*}
-
-which is a concave function.
+which is a convex optimization program since $P(x) = xF^{-1}(1-x)$ is concave
+for regular distributions.
\paragraph{Multiple-item case.} The multiple-agent multiple-item setting is not
fully understood yet, but the revenue function $\Rev(\hat{x},F)$ defined above still