imported>Etone |
imported>TCSseminar |
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| *作业电子版于2019/11/26 23:59 之前提交到邮箱 <font color=blue>njuadvalg@163.com</font>
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| *每道题目的解答都要有<font color="red" size=5>完整的解题过程</font>。中英文不限。
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| == Problem 1== | | {| class="wikitable" |
| Let <math>G(V,E)</math> be an undirected graph with positive edge weights <math>w:E\to\mathbb{Z}^+</math>. Given a partition of <math>V</math> into <math>k</math> disjoint subsets <math>S_1,S_2,\ldots,S_k</math>, we define
| | | 161140077 || 张昕渊 |
| :<math>w(S_1,S_2,\ldots,S_k)=\sum_{uv\in E\atop \exists i\neq j: u\in S_i,v\in S_j}w(uv)</math>
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| as the cost of the '''<math>k</math>-cut''' <math>\{S_1,S_2,\ldots,S_k\}</math>. Our goal is to find a <math>k</math>-cut with maximum cost.
| | | DZ1833022 || 王国华 |
| # Give a poly-time greedy algorithm for finding the weighted max <math>k</math>-cut. Prove that the approximation ratio is <math>(1-1/k)</math>.
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| # Consider the following local search algorithm for the weighted max cut (max 2-cut).
| | | mf1933074 || 乔裕哲 |
| ::Fill in the blank parenthesis. Give an analysis of the running time of the algorithm. And prove that the approximation ratio is 0.5.
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| start with an arbitrary bipartition of <math>V</math> into disjoint <math>S_0,S_1</math>;
| | | MG1933029 || 蒋松儒 |
| while (true) do
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| if <math>\exists i\in\{0,1\}</math> and <math>v\in S_i</math> such that <font color=red>(______________)</font>
| | | 161240059 || 王淳扬 |
| then <math>v</math> leaves <math>S_i</math> and joins <math>S_{1-i}</math>;
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| continue;
| | | MF1933042 || 江会煜 |
| end if
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| break;
| | | MG1933011 || 杜卓轩 |
| end
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| | | | MF1933034 || 侯松林 |
| == Problem 2==
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| Given <math>m</math> subsets <math>S_1,S_2,\ldots, S_m\subseteq U</math> of a universe <math>U</math> of size <math>n</math>, we want to find a <math>C\subseteq\{1,2,\ldots, {m}\}</math> of fixed size <math>k=|C|</math> with the maximum '''coverage''' <math>\left|\bigcup_{i\in C}S_i\right|</math>.
| | | 171860558 || 董杨静 |
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| * Give a poly-time greedy algorithm for the problem. Prove that the approximation ratio is <math>1-(1-1/k)^k>1-1/e</math>.
| | | mg1933024 || 黄开乐 |
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| | | | 161190005 || 陈楚阳 |
| == Problem 3==
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| In the ''maximum directed cut'' (MAX-DICUT) problem, we are given as input a directed graph <math>G(V,E)</math>. The goal is to partition <math>V</math> into disjoint <math>S</math> and <math>T</math> so that the number of edges in <math>E(S,T)=\{(u,v)\in E\mid u\in S, v\in T\}</math> is maximized. The following is the integer program for MAX-DICUT:
| | | mg1933086 || 余浩宇 |
| :::<math>
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| \begin{align}
| | | MG1933031 || 金力为 |
| \text{maximize} &&& \sum_{(u,v)\in E}y_{u,v}\\
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| \text{subject to} && y_{u,v} &\le x_u, & \forall (u,v)&\in E,\\
| | | MG1933039 || 李一凡 |
| && y_{u,v} &\le 1-x_v, & \forall (u,v)&\in E,\\
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| && x_v &\in\{0,1\}, & \forall v&\in V,\\
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| && y_{u,v} &\in\{0,1\}, & \forall (u,v)&\in E.
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| \end{align}
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| </math>
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| Let <math>x_v^*,y_{u,v}^*</math> denote the optimal solution to the '''LP-relaxation''' of the above integer program.
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| * Apply the randomized rounding such that for every <math>v\in V</math>, <math>\hat{x}_v=1</math> independently with probability <math>x_v^*</math>. Analyze the approximation ratio (between the expected size of the random cut and OPT).
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| * Apply another randomized rounding such that for every <math>v\in V</math>, <math>\hat{x}_v=1</math> independently with probability <math>1/4+x_v^*/2</math>. Analyze the approximation ratio for this algorithm.
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| == Problem 4==
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| Recall the MAX-SAT problem and its integer program:
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| :::<math>
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| \begin{align}
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| \text{maximize} &&& \sum_{j=1}^my_j\\
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| \text{subject to} &&& \sum_{i\in S_j^+}x_i+\sum_{i\in S_j^-}(1-x_i)\ge y_j, && 1\le j\le m,\\
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| &&& x_i\in\{0,1\}, && 1\le i\le n,\\
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| &&& y_j\in\{0,1\}, && 1\le j\le m.
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| \end{align}
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| </math>
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| Recall that <math>S_j^+,S_j^-\subseteq\{1,2,\ldots,n\}</math> are the respective sets of variables appearing positively and negatively in clause <math>j</math>.
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| Let <math>x_i^*,y_j^*</math> denote the optimal solution to the '''LP-relaxation''' of the above integer program. In our class we learnt that if <math>\hat{x}_i</math> is round to 1 independently with probability <math>x_i^*</math>, we have approximation ratio <math>1-1/\mathrm{e}</math>.
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| We consider a generalized rounding scheme such that every <math>\hat{x}_i</math> is round to 1 independently with probability <math>f(x_i^*)</math> for some function <math>f:[0,1]\to[0,1]</math> to be specified.
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| * Suppose <math>f(x)</math> is an arbitrary function satisfying that <math>1-4^{-x}\le f(x)\le 4^{x-1}</math> for any <math>x\in[0,1]</math>. Show that with this rounding scheme, the approximation ratio (between the expected number of satisfied clauses and OPT) is at least <math>3/4</math>.
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| * Derandomize this algorithm through conditional expectation and give a deterministic polynomial time algorithm with approximation ratio <math>3/4</math>.
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| * Is it possible that for some more clever <math>f</math> we can do better than this? Try to justify your argument.
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| ==Problem 5 ==
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| The following is the weighted version of set cover problem:
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| Given <math>m</math> subsets <math>S_1,S_2,\ldots,S_m\subseteq U</math>, where <math>U</math> is a universe of size <math>n=|U|</math>, and each subset <math>S_i</math> is assigned a positive weight <math>w_i>0</math>, the goal is to find a <math>C\subseteq\{1,2,\ldots,m\}</math> such that <math>U=\bigcup_{i\in C}S_i</math> and the total weight <math>\sum_{I\in C}w_i</math> is minimized.
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| * Give an integer program for the problem and its LP relaxation.
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| * Consider the following idea of randomized rounding: independently round each fractional value to <math>\{0,1\}</math> with the probability of the fractional value itself; and repeatedly apply this process to the variables rounded to 0 in previous iterations until <math>U</math> is fully covered. Show that this can return a set cover with <math>O(\log n)</math> approximation ratio with probability at least <math>0.99</math>.
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