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| author | Thibaut Horel <thibaut.horel@gmail.com> | 2013-07-05 16:52:28 +0200 |
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| committer | Thibaut Horel <thibaut.horel@gmail.com> | 2013-07-05 16:52:28 +0200 |
| commit | 23351f5b605e351d745766817b225b6930035d27 (patch) | |
| tree | bf2e42224a657f77c8b33b335d15544d22988d21 | |
| parent | 385334421230a9a4f7422817cc2fae224fd561da (diff) | |
| download | recommendation-23351f5b605e351d745766817b225b6930035d27.tar.gz | |
Adding counterexample
| -rw-r--r-- | counterexample.tex | 61 | ||||
| -rw-r--r-- | myerson.tex | 1 | ||||
| -rw-r--r-- | paper.tex | 8 |
3 files changed, 68 insertions, 2 deletions
diff --git a/counterexample.tex b/counterexample.tex new file mode 100644 index 0000000..95f6a45 --- /dev/null +++ b/counterexample.tex @@ -0,0 +1,61 @@ +We give a counterxample of the monotonicity of the maximum allocation rule +defined in \eqref{eq:max-algorithm} for the value function $V$ defined in +\eqref{obj}. We denote by $(e_1, e_2, e_3)$ the canonical basis of $\reals^3$ +and define the following feature vectors: $x_1=e_1$, +$x_2=\frac{1}{\sqrt{2}}\cos\frac{\pi}{5}e_2 ++ \frac{1}{\sqrt{2}}\sin\frac{\pi}{5}e_3$, $x_3=\frac{1}{\sqrt{2}}e_2$ and $x_4 += \frac{1}{2}e_3$, with associated costs $c_1 = \frac{5}{2}$, $c_2=c_3 = 1$ and +$c_4=\frac{2}{3}$. We also assume that the budget of the auctioneer is +$B=\frac{5}{2}$. + +Note that $V(x_i) = \log(1+\|x_i\|^2)$, so $x_1$ is the point of maximum value. +Let us now compute the output of the greedy heuristic. We have: +\begin{equation}\label{eq:local-bazinga} + \frac{V(x_1)}{c_1} \simeq 0.277,\; + \frac{V(x_2)}{c_2}= \frac{V(x_3)}{c_3} \simeq 0.405,\; + \frac{V(x_4)}{c_4} \simeq 0.335 +\end{equation} +so the greedy heuristic will start by selecting $x_2$ or $x_3$. Without loss of +generality, we can assume that it selected $x_2$. From the Sherman-Morrison +formula we get: +\begin{displaymath} + V(\{x_i, x_j\}) - V(x_i) = \log\bigg(1+ \|x_j\|^2 + - \frac{\ip{x_i}{x_j}^2}{1+\|x_i\|^2}\bigg) +\end{displaymath} +In particular, when $x_i$ and $x_j$ are orthogonal $V(\{x_i, x_j\}) = V(x_j)$. +This allows us to compute: +\begin{displaymath} + \frac{V(\{x_2,x_3\})-V(x_2)}{c_3}=\log\bigg(1+\frac{1}{2} + - \frac{1}{6}\cos^2\frac{\pi}{5}\bigg)\simeq 0.329 +\end{displaymath} +\begin{displaymath} + \frac{V(\{x_2,x_4\})-V(x_2)}{c_4}=\frac{3}{2}\log\bigg(1+\frac{1}{4} + - \frac{1}{12}\sin^2\frac{\pi}{5}\bigg)\simeq 0.299 +\end{displaymath} +Note that at this point $x_1$ cannot be selected without exceding the budget. +Hence, the greedy heuristic will add $x_3$ to the greedy solution and returns +the set $\{x_2, x_3\}$ with value: +\begin{displaymath} + V(\{x_2, x_3\}) = V(x_2) + V(\{x_2, x_3\}) - V(x_2)\simeq 0.734 +\end{displaymath} +In contrast, $V(x_1) \simeq 0.693$ so the mechanism will allocate to $\{x_2, +x_3\}$. + +Let us now assume that user $3$ reduces her cost. It comes from +\eqref{eq:local-bazinga} that the greedy heuristic will start by adding her to +the greedy solution. Furthermore: +\begin{displaymath} + \frac{V(\{x_3,x_2\})-V(x_3)}{c_2}=\log\bigg(1+\frac{1}{2} + - \frac{1}{6}\cos^2\frac{\pi}{5}\bigg)\simeq 0.329 +\end{displaymath} +\begin{displaymath} + \frac{V(\{x_3,x_4\})-V(x_3)}{c_4} + =\frac{3}{2}\log\bigg(1+\frac{1}{4}\bigg)\simeq 0.334 +\end{displaymath} +Hence, the greedy solution will be $\{x_3, x_4\}$ with value: +\begin{displaymath} + V(\{x_3, x_4\}) = V(x_3) + V(\{x_3, x_4\}) - V(x_3)\simeq 0.628 +\end{displaymath} +As a consequence the mechanism will allocate to user $1$ in this case. By +reducing her cost, user 3, who was previously allocated, is now rejected by the +mechanism. This contradicts its monotonicity. diff --git a/myerson.tex b/myerson.tex new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/myerson.tex @@ -0,0 +1 @@ + @@ -10,11 +10,11 @@ \usepackage[pagebackref=true,breaklinks=true,colorlinks=true]{hyperref} \title{Budget Feasible Mechanisms for Experimental Design} \author{ - Thibaut Horel\\École Normale Supérieure\\\texttt{thibaut.horel@ens.fr} + Thibaut Horel\\École Normale Supérieure\\\texttt{thibaut.horel@normalesup.org} \and Stratis Ioannidis\\Technicolor\\\texttt{stratis.ioannidis@technicolor.com} \and - S. Muthukrishnan\\Rutgers University\\\texttt{muthu@cs.rutgers.edu} + S. Muthukrishnan\\Rutgers University--Microsoft Research\\\texttt{muthu@cs.rutgers.edu} } \begin{document} @@ -41,6 +41,10 @@ \bibliography{notes} \appendix \input{appendix} +\section{Non-Monotonicity of the Maximum Allocation Rule}\label{sec:monotonicity} +\input{counterexample} +\section{Proof of Lemma~\ref{thm:myerson-variant}}\label{sec:myerson} +\input{myerson} \section{Extensions}\label{sec:ext} \input{general} |
