高级算法 (Fall 2019)/Problem Set 3

• 作业电子版于2019/11/26 23:59 之前提交到邮箱 njuadvalg@163.com
• 每道题目的解答都要有完整的解题过程。中英文不限。

Problem 1

Let [math]\displaystyle{ G(V,E) }[/math] be an undirected graph with positive edge weights [math]\displaystyle{ w:E\to\mathbb{Z}^+ }[/math]. Given a partition of [math]\displaystyle{ V }[/math] into [math]\displaystyle{ k }[/math] disjoint subsets [math]\displaystyle{ S_1,S_2,\ldots,S_k }[/math], we define

[math]\displaystyle{ 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]

as the cost of the [math]\displaystyle{ k }[/math]-cut [math]\displaystyle{ \{S_1,S_2,\ldots,S_k\} }[/math]. Our goal is to find a [math]\displaystyle{ k }[/math]-cut with maximum cost.

1. Give a poly-time greedy algorithm for finding the weighted max [math]\displaystyle{ k }[/math]-cut. Prove that the approximation ratio is [math]\displaystyle{ (1-1/k) }[/math].
2. Consider the following local search algorithm for the weighted max cut (max 2-cut).

Fill in the blank parenthesis. Give an analysis of the running time of the algorithm. And prove that the approximation ratio is 0.5.

start with an arbitrary bipartition of [math]\displaystyle{ V }[/math] into disjoint [math]\displaystyle{ S_0,S_1 }[/math];
while (true) do
   if [math]\displaystyle{ \exists i\in\{0,1\} }[/math] and [math]\displaystyle{ v\in S_i }[/math] such that (______________)
      then [math]\displaystyle{ v }[/math] leaves [math]\displaystyle{ S_i }[/math] and joins [math]\displaystyle{ S_{1-i} }[/math];
      continue;
   end if
   break;
end

Problem 2

Given [math]\displaystyle{ m }[/math] subsets [math]\displaystyle{ S_1,S_2,\ldots, S_m\subseteq U }[/math] of a universe [math]\displaystyle{ U }[/math] of size [math]\displaystyle{ n }[/math], we want to find a [math]\displaystyle{ C\subseteq\{1,2,\ldots, {m}\} }[/math] of fixed size [math]\displaystyle{ k=|C| }[/math] with the maximum coverage [math]\displaystyle{ \left|\bigcup_{i\in C}S_i\right| }[/math].

• Give a poly-time greedy algorithm for the problem. Prove that the approximation ratio is [math]\displaystyle{ 1-(1-1/k)^k\gt 1-1/e }[/math].

Problem 3

In the maximum directed cut (MAX-DICUT) problem, we are given as input a directed graph [math]\displaystyle{ G(V,E) }[/math]. The goal is to partition [math]\displaystyle{ V }[/math] into disjoint [math]\displaystyle{ S }[/math] and [math]\displaystyle{ T }[/math] so that the number of edges in [math]\displaystyle{ 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:

[math]\displaystyle{ \begin{align} \text{maximize} &&& \sum_{(u,v)\in E}y_{u,v}\\ \text{subject to} && y_{u,v} &\le x_u, & \forall (u,v)&\in E,\\ && y_{u,v} &\le 1-x_v, & \forall (u,v)&\in E,\\ && x_v &\in\{0,1\}, & \forall v&\in V,\\ && y_{u,v} &\in\{0,1\}, & \forall (u,v)&\in E. \end{align} }[/math]

Let [math]\displaystyle{ x_v^*,y_{u,v}^* }[/math] denote the optimal solution to the LP-relaxation of the above integer program.

• Apply the randomized rounding such that for every [math]\displaystyle{ v\in V }[/math], [math]\displaystyle{ \hat{x}_v=1 }[/math] independently with probability [math]\displaystyle{ x_v^* }[/math]. Analyze the approximation ratio (between the expected size of the random cut and OPT).
• Apply another randomized rounding such that for every [math]\displaystyle{ v\in V }[/math], [math]\displaystyle{ \hat{x}_v=1 }[/math] independently with probability [math]\displaystyle{ 1/4+x_v^*/2 }[/math]. Analyze the approximation ratio for this algorithm.

Problem 4

Recall the MAX-SAT problem and its integer program:

[math]\displaystyle{ \begin{align} \text{maximize} &&& \sum_{j=1}^my_j\\ \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,\\ &&& x_i\in\{0,1\}, && 1\le i\le n,\\ &&& y_j\in\{0,1\}, && 1\le j\le m. \end{align} }[/math]

Recall that [math]\displaystyle{ S_j^+,S_j^-\subseteq\{1,2,\ldots,n\} }[/math] are the respective sets of variables appearing positively and negatively in clause [math]\displaystyle{ j }[/math].

Let [math]\displaystyle{ 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]\displaystyle{ \hat{x}_i }[/math] is round to 1 independently with probability [math]\displaystyle{ x_i^* }[/math], we have approximation ratio [math]\displaystyle{ 1-1/\mathrm{e} }[/math].

We consider a generalized rounding scheme such that every [math]\displaystyle{ \hat{x}_i }[/math] is round to 1 independently with probability [math]\displaystyle{ f(x_i^*) }[/math] for some function [math]\displaystyle{ f:[0,1]\to[0,1] }[/math] to be specified.

• Suppose [math]\displaystyle{ f(x) }[/math] is an arbitrary function satisfying that [math]\displaystyle{ 1-4^{-x}\le f(x)\le 4^{x-1} }[/math] for any [math]\displaystyle{ 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]\displaystyle{ 3/4 }[/math].
• Derandomize this algorithm through conditional expectation and give a deterministic polynomial time algorithm with approximation ratio [math]\displaystyle{ 3/4 }[/math].
• Is it possible that for some more clever [math]\displaystyle{ f }[/math] we can do better than this? Try to justify your argument.

Problem 5

The following is the weighted version of set cover problem:

Given [math]\displaystyle{ m }[/math] subsets [math]\displaystyle{ S_1,S_2,\ldots,S_m\subseteq U }[/math], where [math]\displaystyle{ U }[/math] is a universe of size [math]\displaystyle{ n=|U| }[/math], and each subset [math]\displaystyle{ S_i }[/math] is assigned a positive weight [math]\displaystyle{ w_i\gt 0 }[/math], the goal is to find a [math]\displaystyle{ C\subseteq\{1,2,\ldots,m\} }[/math] such that [math]\displaystyle{ U=\bigcup_{i\in C}S_i }[/math] and the total weight [math]\displaystyle{ \sum_{I\in C}w_i }[/math] is minimized.

• Give an integer program for the problem and its LP relaxation.
• Consider the following idea of randomized rounding: independently round each fractional value to [math]\displaystyle{ \{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]\displaystyle{ U }[/math] is fully covered. Show that this can return a set cover with [math]\displaystyle{ O(\log n) }[/math] approximation ratio with probability at least [math]\displaystyle{ 0.99 }[/math].