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810784419a
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master
Author | SHA1 | Date | |
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19c544e04c | |||
642260d568 | |||
98f0985195 | |||
848a0b5c6b | |||
114b2896eb | |||
f464f47e17 | |||
421bbca37d | |||
0ddd930119 | |||
1361b5fac1 |
@@ -7,19 +7,19 @@ jobs:
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steps:
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steps:
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- name: Check out the repository
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- name: Check out the repository
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uses: actions/checkout@v4
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uses: http://localhost:3000/sxlxc/checkout@v4
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- name: Compile LaTeX using local TeX Live
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- name: Compile LaTeX using local TeX Live
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# These commands run directly in your machine's shell
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# These commands run directly in your machine's shell
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run: |
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run: |
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echo "Compiling document..."
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echo "Compiling document..."
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latexmk -pdf poster.tex
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latexmk poster.tex -pdf
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latexmk -pdf slides.tex
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latexmk slides.tex -pdf
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- name: List files in the workspace
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- name: List files in the workspace
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run: ls -l
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run: ls -l
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- uses: akkuman/gitea-release-action@v1
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- uses: http://localhost:3000/sxlxc/gitea-release-action@v1
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with:
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with:
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body: ''
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body: ''
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prerelease: true
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prerelease: true
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@@ -7,8 +7,8 @@
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\ProvidesPackage{beamerthemePoster}
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\ProvidesPackage{beamerthemePoster}
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\RequirePackage[dvipsnames]{xcolor}
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\RequirePackage[dvipsnames]{xcolor}
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% \RequirePackage{tikz}
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\RequirePackage{tikz}
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% \usetikzlibrary{arrows,decorations.pathmorphing,backgrounds,calc}
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\usetikzlibrary{arrows,decorations.pathmorphing,backgrounds,calc}
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% \RequirePackage{lmodern}
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% \RequirePackage{lmodern}
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% \RequirePackage{textcomp}
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% \RequirePackage{textcomp}
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\RequirePackage{amsmath,amssymb,amsthm}
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\RequirePackage{amsmath,amssymb,amsthm}
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@@ -185,7 +185,7 @@
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\end{center}
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\end{center}
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\end{minipage}
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\end{minipage}
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\end{center}
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\end{center}
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\vskip1cm
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\vskip0.8cm
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}
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}
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%
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%
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@@ -195,11 +195,11 @@
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\renewcommand{\section}[1]{
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\renewcommand{\section}[1]{
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\par\vskip\medskipamount%
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\par\vskip\medskipamount%
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%
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%
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% \begin{flushleft}
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\begin{flushleft}
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% \begin{tikzpicture}[remember picture,overlay]
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\begin{tikzpicture}[remember picture,overlay]
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% \shade [inner color=color2,outer color=color3] (0,0) rectangle (\columnwidth,0.3cm);
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\shade [inner color=color2,outer color=color3] (0,0) rectangle (\columnwidth,0.3cm);
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% \end{tikzpicture}
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\end{tikzpicture}
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% \end{flushleft}
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\end{flushleft}
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% \vspace{5pt}
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% \vspace{5pt}
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%
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%
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\begin{center}
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\begin{center}
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@@ -208,12 +208,12 @@
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{\parskip0pt\par}
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{\parskip0pt\par}
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\end{center}
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\end{center}
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%
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%
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% \begin{flushleft}
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\begin{flushleft}
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% \vskip-1cm
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\vskip-1cm
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% \begin{tikzpicture}[remember picture,overlay]
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\begin{tikzpicture}[remember picture,overlay]
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% \shade [inner color=color2,outer color=color3] (0,0) rectangle (\columnwidth,0.3cm);
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\shade [inner color=color2,outer color=color3] (0,0) rectangle (\columnwidth,0.3cm);
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% \end{tikzpicture}
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\end{tikzpicture}
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% \end{flushleft}
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\end{flushleft}
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% \vspace{2.5pt}
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% \vspace{2.5pt}
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%
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%
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{\parskip0pt\par}
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{\parskip0pt\par}
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BIN
image/chatgpt.png
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BIN
image/chatgpt.png
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Binary file not shown.
After Width: | Height: | Size: 4.5 MiB |
BIN
image/poster.png
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BIN
image/poster.png
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Binary file not shown.
After Width: | Height: | Size: 428 KiB |
16
poster.tex
16
poster.tex
@@ -39,31 +39,31 @@
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\begin{document}
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\begin{document}
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% larger font
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% larger font
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\large
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\large
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\begin{frame}[t]
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\begin{frame}[t]
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\vspace{-3cm}
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\begin{multicols}{2}
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\begin{multicols}{2}
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\section{Problem}
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\section{Problem}
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We consider the incentive allocation problem with additional constraints.
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We consider the incentive allocation problem with additional constraints.
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\textbf{Input}: A set of coupons $E=\bigcupdot_i E_i$, where each coupon $e\in E$ has value and cost $v_e,c_e\in \mathbb{Z}_+$. Budget $B\in \mathbb{Z}_+$. Constraints $\mathcal F_i$ on each subset $E_i$.
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\textbf{Input}: A set of coupons $E=\bigcupdot_i E_i$, where each coupon $e\in E$ has a value and a cost $v_e,c_e\in \mathbb{Z}_+$. Budget $B\in \mathbb{Z}_+$. Constraints $\mathcal F_i$ on each subset $E_i$.
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\textcolor{Gray}{
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\textcolor{Gray}{
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\textbf{Output}: A subset $X\subset E$ of coupons that maximizes the total value $\sum_{e\in X}v_e$ while satisfying $\sum_{e\in X}c_e\leq B$ and additional constraints $X\cap E_i\in \mathcal F_i$.
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\textbf{Output}: A subset $X\subset E$ of coupons that maximizes the total value $\sum_{e\in X}v_e$ while satisfying the budget constraint $\sum_{e\in X}c_e\leq B$ and additional constraints $X\cap E_i\in \mathcal F_i$.
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}
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}
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This problem is NP-hard. Consider its LP relaxation.
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This problem is NP-hard. Consider its LP relaxation.
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\begin{equation}\label{LP}
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\begin{equation*}\label{LP}
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\begin{aligned}
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\begin{aligned}
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\tau(B)=\max_x&\; & v\cdot x& & & \\
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\tau(B)=\max_x&\; & v\cdot x& & & \\
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s.t.&\; & c \cdot x &\leq B & &\\
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s.t.&\; & c \cdot x &\leq B & &\\
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& & x_{E_i}&\in \conv(\mathcal{F}_i) & &\;\forall i\in [n]\\
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& & x_{E_i}&\in \conv(\mathcal{F}_i) & &\;\forall i\in [n]\\
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& & x&\in [0,1]^m & &
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& & x&\in [0,1]^m & &
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\end{aligned}
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\end{aligned}
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\end{equation}
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\end{equation*}
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\textbf{Output}: The entire curve $\tau(B)$ for $B\in [0,\infty)$.
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\textbf{Output}: The entire curve $\tau(B)$ for $B\in [0,\infty)$.
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We consider 3 cases of additional constraints $x_{E_i}\in \mathcal{F}_i$ :
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We consider 3 cases of additional constraints $x_{E_i}\in \mathcal{F}_i$ :
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@@ -101,17 +101,17 @@ We consider 3 cases of additional constraints $x_{E_i}\in \mathcal{F}_i$ :
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\end{table}
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\end{table}
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\section{Methods}
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\section{Methods}
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The idea is to take advantage of the independence among the constraints $\mathcal{F}_i$ and to reduce the optimization problem to one in computational geometry.
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The idea is to take advantage of the independence among the constraints $\mathcal{F}_i$ and reduce the optimization problem to one in computational geometry.
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\textcolor{DarkOrchid}{\textit{Signature Function.}} Let $f_i(\lambda) = \max\{(v_{E_i}-\lambda c_{E_i}) x | x\in \conv(\mathcal F_i) \}$ be the signature function of agent $i$. The signature function is piecewise-linar and convex.
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\textcolor{DarkOrchid}{\textit{Signature Function.}} Let $f_i(\lambda) = \max\{(v_{E_i}-\lambda c_{E_i}) x | x\in \conv(\mathcal F_i) \}$ be the signature function of agent $i$. The signature function is piecewise-linar and convex.
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\textcolor{DarkOrchid}{\textit{Lagrangian Dual.}} The Lagrangian dual of LP\autoref{LP} is therefore
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\textcolor{DarkOrchid}{\textit{Lagrangian Dual.}} The Lagrangian dual of LP\autoref{LP} is therefore
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\begin{equation}
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\begin{equation*}
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\label{eq:Lagrangiandual}
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\label{eq:Lagrangiandual}
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\begin{aligned}
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\begin{aligned}
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\min_{\lambda} \left( B\lambda+\sum_i f_i(\lambda)\right).
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\min_{\lambda} \left( B\lambda+\sum_i f_i(\lambda)\right).
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\end{aligned}
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\end{aligned}
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\end{equation}
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\end{equation*}
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\begin{theorem}[4]\large
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\begin{theorem}[4]\large
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$\tau(B)$ is piecewise-linear and concave.
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$\tau(B)$ is piecewise-linear and concave.
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36
slides.tex
36
slides.tex
@@ -4,6 +4,15 @@
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\usepackage{algo}
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\usepackage{algo}
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\usepackage{soul}
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\usepackage{soul}
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% \usepackage{cancel}
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% \usepackage{cancel}
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\newcommand{\munepsfig}[3][scale=1.0]{% <===============================
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\begin{figure}[!htbp]
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\centering
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\vspace{2mm}
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\setlength{\fboxrule}{#3} % <===================================
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\framebox{\includegraphics[#1]{#2.png}} % <=====================
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\label{fig:#2}
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\end{figure}
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}
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\title[Incentive allocation]{Large-Scale Trade-Off Curve Computation for Incentive Allocation with Cardinality and Matroid Constraints}
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\title[Incentive allocation]{Large-Scale Trade-Off Curve Computation for Incentive Allocation with Cardinality and Matroid Constraints}
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\date{August 30, 2025}
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\date{August 30, 2025}
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@@ -28,8 +37,9 @@ A ride sharing company wants to send riders promotional coupons in the hope of m
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% each agent gets at most 1 coupon.
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% each agent gets at most 1 coupon.
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\begin{figure}
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\begin{figure}
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placeholder\\
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\includegraphics[width=.7\textwidth]{image/chatgpt.png}
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\includegraphics[width=.5\textwidth]{image/landscape.png}
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\scriptsize Image courtesy: ChatGPT-5
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\end{figure}
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\end{figure}
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\end{frame}
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\end{frame}
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@@ -37,24 +47,24 @@ placeholder\\
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\begin{frame}{Multiple-choice knapsack}
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\begin{frame}{Multiple-choice knapsack}
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\textbf{Input}: $n$ sets of coupons $K_1,\dots,K_n$. Each coupon $e\in K_i$ has a non-negative cost $c_e\in \Z_+$ and value $v_e\in \Z_+$. A positive budget $b\in \Z_+$.
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\textbf{Input}: $n$ sets of coupons $K_1,\dots,K_n$. Each coupon $e\in K_i$ has a non-negative cost $c_e\in \Z_+$ and value $v_e\in \Z_+$. A positive budget $b\in \Z_+$.
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\textbf{Output}: A (multi)set of coupons $K$ that maximizes the total value $\sum_{e\in K} c_e$ while satisfying \textcolor{Red}{$|K\cap K_i|\leq 1$} and $\sum_{e\in K} c_e\leq b$.
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\textbf{Output}: A subset of coupons $K$ that maximizes the total value $\sum_{e\in K} v_e$ while satisfying \textcolor{Red}{$|K\cap K_i|\leq 1$} and $\sum_{e\in K} c_e\leq b$.
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\vspace{1em}
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\vspace{1em}
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\pause
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\pause
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Three problems with this modeling:
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Three problems with this formulation:
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\begin{enumerate}
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\begin{enumerate}
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\item Finding the exact optimum is NP-hard. So we consider solving it approximately.
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\item Finding the exact optimum is NP-hard. So we consider solving it approximately.
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\item Companies may run multiple campaigns at the same time. So a trade-off curve between budget and profit will be useful.
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\item Companies may run multiple campaigns at the same time. So a trade-off curve between budget and profit will be useful.
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\item The multiple-choice constraint \textcolor{Red}{$|K\cap K_i|\leq 1$} is too weak for real applications.
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\item The multiple-choice constraint \textcolor{Red}{$|K\cap K_i|\leq 1$} is too strong for real-world applications.
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\end{enumerate}
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\end{enumerate}
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\end{frame}
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\end{frame}
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\begin{frame}{Linear programming formulation}
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\begin{frame}{Linear programming relaxation}
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\textcolor{gray}{
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% \textcolor{gray}{
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\textbf{Input}: $n$ sets of coupons $K_1,\dots,K_n$. Each coupon $e\in K_i$ has a non-negative cost $c_e\in \Z_+$ and value $v_e\in \Z_+$. \st{A positive budget $b\in \Z_+$.}
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\textbf{Input}: $n$ sets of coupons $K_1,\dots,K_n$. Each coupon $e\in K_i$ has a non-negative cost $c_e\in \Z_+$ and value $v_e\in \Z_+$. \st{A positive budget $b\in \Z_+$.}
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}
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% }
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\pause
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\pause
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\begin{equation*}
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\begin{equation*}
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\begin{aligned}
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\begin{aligned}
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@@ -64,7 +74,7 @@ s.t.& & c\cdot x&\leq b & &\\
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\end{aligned}
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\end{aligned}
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\end{equation*}
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\end{equation*}
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\textbf{Output}: A compact representation of $\tau(b)$.
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\textbf{Output}: function $\tau(b)$ for $b\in (0,+\infty)$.
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\pause
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\pause
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We focus on 2 kinds of constraints of \textcolor{Plum}{$x_{K_i}\in P_{K_i}$}.
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We focus on 2 kinds of constraints of \textcolor{Plum}{$x_{K_i}\in P_{K_i}$}.
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@@ -88,6 +98,12 @@ $k=O(mp^{1/3})$ and $\tau$ can be computed in $O((k+m)\log m)$ time.
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\end{frame}
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\end{frame}
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\begin{frame}[plain]
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\begin{frame}[plain]
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poster
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\centering
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\Large \textit{Let's discuss this in detail at my poster!}
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\munepsfig[width=.8\linewidth]{image/poster}{1pt}
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% \begin{figure}
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% \includegraphics[width=0.8\linewidth]{image/poster.png}
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% \end{figure}
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\end{frame}
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\end{frame}
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\end{document}
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\end{document}
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Reference in New Issue
Block a user