# Tag Info

## Hot answers tagged approximation-hardness

8

The powering step fails. After the powering, each vertex is labeled with a neighborhood of the original graph. each edge checks that its endpoints agree on the intersection of their neighborhoods, and that this labeling satisfies the edges in this intersection. However, the edge cannot check anything about part of the labeling that lies outside the ...

4

The best approximation ratio that can be achieved in polynomial time should be $\Theta(\log n)$ where $n$ is the number of vertices. This can be seen by standard reductions from the Set Cover problem which is NP-hard to approximate within a factor of $(1-\alpha)\cdot N$ where $N$ is the input size . First, we use the standard reduction from Set Cover to ...

4

The following website seems to be no longer maintained, but it is still a useful resource because it covers many problems: http://www.csc.kth.se/~viggo/problemlist/

2

Recently, I came across the following survey: Recent Developments in Approximation Algorithms for Facility Location and Clustering Problems The authors only discuss the major results and mention some interesting open problems at the end.

2

The answer to Question (1) is no. The answer to Question (2) is yes. Here are the details. I'll work with the following equivalent problem formulations. For the input, we are given $n$ pairs of values $(v_1, w_1), (v_2, w_2), \ldots, (v_n, w_n)$ in $\mathbb R^2_+$. Problem A. Find $\max\big\{ \sum_{i\not\in S} v_i : S\subseteq [n],\, \sum_{i\not\in S} w_i ... 1 After a bit more searching, it appears that what I'm looking for is unlikely to exist. In , it is proven that approximating the minimum maximal independence number (which is equivalent to the minimum size of an independent dominating set) within a factor of$O(n^{1-\epsilon})$is$\mathrm{NP}$-hard for any$\epsilon > 0$. This remains true even when ... 1 For the non-metric$k$-median problem, we can show a stronger inapproximability result than$O(\log n)$. The following is a stronger claim: Main Claim: The non-metric$k$-median problem can not be approximated to any factor better than$n^{c}$, for any constant$c>0$. Proof: The proof follows from the reduction from the hardness of the max$k\$ coverage ...

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